anthropic

Server path: /anthropic | Type: Application | PCID required: Yes

Tools

Tool	Description
`anthropic_analyze_image`	DEPRECATED — Analyze an image using Claude vision capabilities. Use anthropic_create_message with image content blocks instead.
`anthropic_batch_messages`	DEPRECATED — Process multiple messages sequentially. Use anthropic_create_message_batch for proper async batch processing instead.
`anthropic_cancel_message_batch`	Cancel a Message Batch
`anthropic_count_tokens`	Count tokens in a Message
`anthropic_create_message`	Create a Message
`anthropic_create_message_batch`	Create a Message Batch
`anthropic_create_skill`	Create Skill
`anthropic_create_skill_version`	Create Skill Version
`anthropic_delete_file`	Delete File
`anthropic_delete_message_batch`	Delete a Message Batch
`anthropic_delete_skill`	Delete Skill
`anthropic_delete_skill_version`	Delete Skill Version
`anthropic_download_file`	Download File
`anthropic_get_file`	Get File Metadata
`anthropic_get_message_batch`	Retrieve a Message Batch
`anthropic_get_message_batch_results`	Retrieve Message Batch results
`anthropic_get_model`	Get a Model
`anthropic_get_skill`	Get Skill
`anthropic_get_skill_version`	Get Skill Version
`anthropic_list_files`	List Files
`anthropic_list_message_batches`	List Message Batches
`anthropic_list_models`	List Models
`anthropic_list_skill_versions`	List Skill Versions
`anthropic_list_skills`	List Skills
`anthropic_upload_file`	Upload File

anthropic_analyze_image

DEPRECATED — Analyze an image using Claude vision capabilities. Use anthropic_create_message with image content blocks instead. Parameters:

Parameter	Type	Required	Default	Description
`model`	string	No	—	Claude model with vision capabilities (e.g., claude-sonnet-4-5-20250929)
`imageUrl`	string	No	—	URL of image to analyze
`imageBase64`	string	No	—	Base64 encoded image data
`prompt`	string	Yes	—	Question or instruction about the image
`maxTokens`	integer	No	—	Maximum tokens to generate

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "model": {
      "type": "string",
      "description": "Claude model with vision capabilities (e.g., claude-sonnet-4-5-20250929)"
    },
    "imageUrl": {
      "type": "string",
      "description": "URL of image to analyze"
    },
    "imageBase64": {
      "type": "string",
      "description": "Base64 encoded image data"
    },
    "prompt": {
      "type": "string",
      "description": "Question or instruction about the image"
    },
    "maxTokens": {
      "type": "integer",
      "description": "Maximum tokens to generate"
    }
  },
  "required": [
    "PCID",
    "prompt"
  ]
}

anthropic_batch_messages

DEPRECATED — Process multiple messages sequentially. Use anthropic_create_message_batch for proper async batch processing instead. Parameters:

Parameter	Type	Required	Default	Description
`model`	string	Yes	—	Claude model to use (e.g., claude-sonnet-4-5-20250929)
`requests`	object[]	Yes	—	Array of message requests to process sequentially
`temperature`	number	No	—	Sampling temperature (0-1)

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "model": {
      "type": "string",
      "description": "Claude model to use (e.g., claude-sonnet-4-5-20250929)"
    },
    "requests": {
      "type": "array",
      "description": "Array of message requests to process sequentially",
      "items": {
        "type": "object",
        "properties": {
          "messages": {
            "type": "array",
            "description": "Messages for this request",
            "items": {
              "type": "object",
              "properties": {
                "role": {
                  "type": "string",
                  "description": "Message role (user or assistant)"
                },
                "content": {
                  "type": "string",
                  "description": "Message content"
                }
              },
              "required": [
                "role",
                "content"
              ]
            }
          },
          "system": {
            "type": "string",
            "description": "System prompt"
          },
          "maxTokens": {
            "type": "integer",
            "description": "Max tokens for this request"
          }
        },
        "required": [
          "messages"
        ]
      }
    },
    "temperature": {
      "type": "number",
      "description": "Sampling temperature (0-1)"
    }
  },
  "required": [
    "PCID",
    "model",
    "requests"
  ]
}

anthropic_cancel_message_batch

Cancel a Message Batch Parameters:

Parameter	Type	Required	Default	Description
`message_batch_id`	string	Yes	—	ID of the Message Batch.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "message_batch_id": {
      "type": "string",
      "description": "ID of the Message Batch."
    }
  },
  "required": [
    "PCID",
    "message_batch_id"
  ]
}

anthropic_count_tokens

Count tokens in a Message Parameters:

Parameter	Type	Required	Default	Description
`cache_control`	object	No	—	Top-level cache control automatically applies a cache_control marker to the last cacheable block in the request.
`messages`	object[]	Yes	—	Input messages. Our models are trained to operate on alternating `user` and `assistant` conversational turns. When creating a new `Message`, you specify the prior conversational turns with the `messages` parameter, and the model then generates the next `Message` in the conversation. Consecutive `user` or `assistant` turns in your request will be combined into a single turn. Each input message must be an object with a `role` and `content`. You can specify a single `user`-role message, or you can include multiple `user` and `assistant` messages. If the final message uses the `assistant` role, the response content will continue immediately from the content in that message. This can be used to constrain part of the model’s response. Example with a single `user` message: `json [{"role": "user", "content": "Hello, Claude"}]` Example with multiple conversational turns: `json [ {"role": "user", "content": "Hello there."}, {"role": "assistant", "content": "Hi, I'm Claude. How can I help you?"}, {"role": "user", "content": "Can you explain LLMs in plain English?"}, ]` Example with a partially-filled response from Claude: `json [ {"role": "user", "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun"}, {"role": "assistant", "content": "The best answer is ("}, ]` Each input message `content` may be either a single `string` or an array of content blocks, where each block has a specific `type`. Using a `string` for `content` is shorthand for an array of one content block of type `"text"`. The following input messages are equivalent: `json {"role": "user", "content": "Hello, Claude"}` `json {"role": "user", "content": [{"type": "text", "text": "Hello, Claude"}]}` See input examples. Note that if you want to include a system prompt, you can use the top-level `system` parameter — there is no `"system"` role for input messages in the Messages API. There is a limit of 100,000 messages in a single request.
`model`	object	Yes	—	The model that will complete your prompt.\n\nSee models for additional details and options.
`output_config`	object	No	—	Output Config
`system`	object	No	—	System prompt. A system prompt is a way of providing context and instructions to Claude, such as specifying a particular goal or role. See our guide to system prompts.
`thinking`	object	No	—	Configuration for enabling Claude’s extended thinking. When enabled, responses include `thinking` content blocks showing Claude’s thinking process before the final answer. Requires a minimum budget of 1,024 tokens and counts towards your `max_tokens` limit. See extended thinking for details.
`tool_choice`	object	No	—	How the model should use the provided tools. The model can use a specific tool, any available tool, decide by itself, or not use tools at all.
`tools`	any[]	No	—	Definitions of tools that the model may use. If you include `tools` in your API request, the model may return `tool_use` content blocks that represent the model’s use of those tools. You can then run those tools using the tool input generated by the model and then optionally return results back to the model using `tool_result` content blocks. There are two types of tools: client tools and server tools. The behavior described below applies to client tools. For server tools, see their individual documentation as each has its own behavior (e.g., the web search tool). Each tool definition includes: * `name`: Name of the tool. * `description`: Optional, but strongly-recommended description of the tool. * `input_schema`: JSON schema for the tool `input` shape that the model will produce in `tool_use` output content blocks. For example, if you defined `tools` as: `json [ { "name": "get_stock_price", "description": "Get the current stock price for a given ticker symbol.", "input_schema": { "type": "object", "properties": { "ticker": { "type": "string", "description": "The stock ticker symbol, e.g. AAPL for Apple Inc." } }, "required": ["ticker"] } } ]` And then asked the model “What’s the S&P 500 at today?”, the model might produce `tool_use` content blocks in the response like this: `json [ { "type": "tool_use", "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", "name": "get_stock_price", "input": { "ticker": "^GSPC" } } ]` You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an input, and return the following back to the model in a subsequent `user` message: `json [ { "type": "tool_result", "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", "content": "259.75 USD" } ]` Tools can be used for workflows that include running client-side tools and functions, or more generally whenever you want the model to produce a particular JSON structure of output. See our guide for more details.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "cache_control": {
      "type": "object",
      "description": "Top-level cache control automatically applies a cache_control marker to the last cacheable block in the request.",
      "properties": {
        "ttl": {
          "type": "string",
          "description": "The time-to-live for the cache control breakpoint.  This may be one the following values: - `5m`: 5 minutes - `1h`: 1 hour  Defaults to `5m`."
        },
        "type": {
          "type": "string",
          "description": "The type value"
        }
      },
      "required": [
        "type"
      ]
    },
    "messages": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "Input messages.  Our models are trained to operate on alternating `user` and `assistant` conversational turns. When creating a new `Message`, you specify the prior conversational turns with the `messages` parameter, and the model then generates the next `Message` in the conversation. Consecutive `user` or `assistant` turns in your request will be combined into a single turn.  Each input message must be an object with a `role` and `content`. You can specify a single `user`-role message, or you can include multiple `user` and `assistant` messages.  If the final message uses the `assistant` role, the response content will continue immediately from the content in that message. This can be used to constrain part of the model's response.  Example with a single `user` message:  ```json [{\"role\": \"user\", \"content\": \"Hello, Claude\"}] ```  Example with multiple conversational turns:  ```json [   {\"role\": \"user\", \"content\": \"Hello there.\"},   {\"role\": \"assistant\", \"content\": \"Hi, I'm Claude. How can I help you?\"},   {\"role\": \"user\", \"content\": \"Can you explain LLMs in plain English?\"}, ] ```  Example with a partially-filled response from Claude:  ```json [   {\"role\": \"user\", \"content\": \"What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun\"},   {\"role\": \"assistant\", \"content\": \"The best answer is (\"}, ] ```  Each input message `content` may be either a single `string` or an array of content blocks, where each block has a specific `type`. Using a `string` for `content` is shorthand for an array of one content block of type `\"text\"`. The following input messages are equivalent:  ```json {\"role\": \"user\", \"content\": \"Hello, Claude\"} ```  ```json {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"Hello, Claude\"}]} ```  See [input examples](https://docs.claude.com/en/api/messages-examples).  Note that if you want to include a [system prompt](https://docs.claude.com/en/docs/system-prompts), you can use the top-level `system` parameter — there is no `\"system\"` role for input messages in the Messages API.  There is a limit of 100,000 messages in a single request."
    },
    "model": {
      "description": "The model that will complete your prompt.\\n\\nSee [models](https://docs.anthropic.com/en/docs/models-overview) for additional details and options."
    },
    "output_config": {
      "type": "object",
      "description": "Output Config",
      "properties": {
        "effort": {
          "type": "string",
          "description": "How much effort the model should put into its response. Higher effort levels may result in more thorough analysis but take longer.  Valid values are `low`, `medium`, `high`, or `max`."
        },
        "format": {
          "type": "object",
          "description": "A schema to specify Claude's output format in responses. See [structured outputs](https://platform.claude.com/docs/en/build-with-claude/structured-outputs)"
        }
      }
    },
    "system": {
      "description": "System prompt.  A system prompt is a way of providing context and instructions to Claude, such as specifying a particular goal or role. See our [guide to system prompts](https://docs.claude.com/en/docs/system-prompts)."
    },
    "thinking": {
      "description": "Configuration for enabling Claude's extended thinking.  When enabled, responses include `thinking` content blocks showing Claude's thinking process before the final answer. Requires a minimum budget of 1,024 tokens and counts towards your `max_tokens` limit.  See [extended thinking](https://docs.claude.com/en/docs/build-with-claude/extended-thinking) for details."
    },
    "tool_choice": {
      "description": "How the model should use the provided tools. The model can use a specific tool, any available tool, decide by itself, or not use tools at all."
    },
    "tools": {
      "type": "array",
      "description": "Definitions of tools that the model may use.  If you include `tools` in your API request, the model may return `tool_use` content blocks that represent the model's use of those tools. You can then run those tools using the tool input generated by the model and then optionally return results back to the model using `tool_result` content blocks.  There are two types of tools: **client tools** and **server tools**. The behavior described below applies to client tools. For [server tools](https://docs.claude.com/en/docs/agents-and-tools/tool-use/overview\\#server-tools), see their individual documentation as each has its own behavior (e.g., the [web search tool](https://docs.claude.com/en/docs/agents-and-tools/tool-use/web-search-tool)).  Each tool definition includes:  * `name`: Name of the tool. * `description`: Optional, but strongly-recommended description of the tool. * `input_schema`: [JSON schema](https://json-schema.org/draft/2020-12) for the tool `input` shape that the model will produce in `tool_use` output content blocks.  For example, if you defined `tools` as:  ```json [   {     \"name\": \"get_stock_price\",     \"description\": \"Get the current stock price for a given ticker symbol.\",     \"input_schema\": {       \"type\": \"object\",       \"properties\": {         \"ticker\": {           \"type\": \"string\",           \"description\": \"The stock ticker symbol, e.g. AAPL for Apple Inc.\"         }       },       \"required\": [\"ticker\"]     }   } ] ```  And then asked the model \"What's the S&P 500 at today?\", the model might produce `tool_use` content blocks in the response like this:  ```json [   {     \"type\": \"tool_use\",     \"id\": \"toolu_01D7FLrfh4GYq7yT1ULFeyMV\",     \"name\": \"get_stock_price\",     \"input\": { \"ticker\": \"^GSPC\" }   } ] ```  You might then run your `get_stock_price` tool with `{\"ticker\": \"^GSPC\"}` as an input, and return the following back to the model in a subsequent `user` message:  ```json [   {     \"type\": \"tool_result\",     \"tool_use_id\": \"toolu_01D7FLrfh4GYq7yT1ULFeyMV\",     \"content\": \"259.75 USD\"   } ] ```  Tools can be used for workflows that include running client-side tools and functions, or more generally whenever you want the model to produce a particular JSON structure of output.  See our [guide](https://docs.claude.com/en/docs/tool-use) for more details."
    }
  },
  "required": [
    "PCID",
    "messages",
    "model"
  ]
}

anthropic_create_message

Create a Message Parameters:

Parameter	Type	Required	Default	Description
`cache_control`	object	No	—	Top-level cache control automatically applies a cache_control marker to the last cacheable block in the request.
`container`	string	No	—	Container identifier for reuse across requests.
`inference_geo`	string	No	—	Specifies the geographic region for inference processing. If not specified, the workspace’s `default_inference_geo` is used.
`max_tokens`	integer	Yes	—	The maximum number of tokens to generate before stopping. Note that our models may stop before reaching this maximum. This parameter only specifies the absolute maximum number of tokens to generate. Different models have different maximum values for this parameter. See models for details.
`messages`	object[]	Yes	—	Input messages. Our models are trained to operate on alternating `user` and `assistant` conversational turns. When creating a new `Message`, you specify the prior conversational turns with the `messages` parameter, and the model then generates the next `Message` in the conversation. Consecutive `user` or `assistant` turns in your request will be combined into a single turn. Each input message must be an object with a `role` and `content`. You can specify a single `user`-role message, or you can include multiple `user` and `assistant` messages. If the final message uses the `assistant` role, the response content will continue immediately from the content in that message. This can be used to constrain part of the model’s response. Example with a single `user` message: `json [{"role": "user", "content": "Hello, Claude"}]` Example with multiple conversational turns: `json [ {"role": "user", "content": "Hello there."}, {"role": "assistant", "content": "Hi, I'm Claude. How can I help you?"}, {"role": "user", "content": "Can you explain LLMs in plain English?"}, ]` Example with a partially-filled response from Claude: `json [ {"role": "user", "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun"}, {"role": "assistant", "content": "The best answer is ("}, ]` Each input message `content` may be either a single `string` or an array of content blocks, where each block has a specific `type`. Using a `string` for `content` is shorthand for an array of one content block of type `"text"`. The following input messages are equivalent: `json {"role": "user", "content": "Hello, Claude"}` `json {"role": "user", "content": [{"type": "text", "text": "Hello, Claude"}]}` See input examples. Note that if you want to include a system prompt, you can use the top-level `system` parameter — there is no `"system"` role for input messages in the Messages API. There is a limit of 100,000 messages in a single request.
`metadata`	object	No	—	The metadata value
`model`	object	Yes	—	The model that will complete your prompt.\n\nSee models for additional details and options.
`output_config`	object	No	—	Output Config
`service_tier`	string	No	—	Determines whether to use priority capacity (if available) or standard capacity for this request. Anthropic offers different levels of service for your API requests. See service-tiers for details.
`stop_sequences`	string[]	No	—	Custom text sequences that will cause the model to stop generating. Our models will normally stop when they have naturally completed their turn, which will result in a response `stop_reason` of `"end_turn"`. If you want the model to stop generating when it encounters custom strings of text, you can use the `stop_sequences` parameter. If the model encounters one of the custom sequences, the response `stop_reason` value will be `"stop_sequence"` and the response `stop_sequence` value will contain the matched stop sequence.
`stream`	boolean	No	—	Whether to incrementally stream the response using server-sent events. See streaming for details.
`system`	object	No	—	System prompt. A system prompt is a way of providing context and instructions to Claude, such as specifying a particular goal or role. See our guide to system prompts.
`temperature`	number	No	—	Amount of randomness injected into the response. Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0` for analytical / multiple choice, and closer to `1.0` for creative and generative tasks. Note that even with `temperature` of `0.0`, the results will not be fully deterministic.
`thinking`	object	No	—	Configuration for enabling Claude’s extended thinking. When enabled, responses include `thinking` content blocks showing Claude’s thinking process before the final answer. Requires a minimum budget of 1,024 tokens and counts towards your `max_tokens` limit. See extended thinking for details.
`tool_choice`	object	No	—	How the model should use the provided tools. The model can use a specific tool, any available tool, decide by itself, or not use tools at all.
`tools`	any[]	No	—	Definitions of tools that the model may use. If you include `tools` in your API request, the model may return `tool_use` content blocks that represent the model’s use of those tools. You can then run those tools using the tool input generated by the model and then optionally return results back to the model using `tool_result` content blocks. There are two types of tools: client tools and server tools. The behavior described below applies to client tools. For server tools, see their individual documentation as each has its own behavior (e.g., the web search tool). Each tool definition includes: * `name`: Name of the tool. * `description`: Optional, but strongly-recommended description of the tool. * `input_schema`: JSON schema for the tool `input` shape that the model will produce in `tool_use` output content blocks. For example, if you defined `tools` as: `json [ { "name": "get_stock_price", "description": "Get the current stock price for a given ticker symbol.", "input_schema": { "type": "object", "properties": { "ticker": { "type": "string", "description": "The stock ticker symbol, e.g. AAPL for Apple Inc." } }, "required": ["ticker"] } } ]` And then asked the model “What’s the S&P 500 at today?”, the model might produce `tool_use` content blocks in the response like this: `json [ { "type": "tool_use", "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", "name": "get_stock_price", "input": { "ticker": "^GSPC" } } ]` You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an input, and return the following back to the model in a subsequent `user` message: `json [ { "type": "tool_result", "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", "content": "259.75 USD" } ]` Tools can be used for workflows that include running client-side tools and functions, or more generally whenever you want the model to produce a particular JSON structure of output. See our guide for more details.
`top_k`	integer	No	—	Only sample from the top K options for each subsequent token. Used to remove “long tail” low probability responses. Learn more technical details here. Recommended for advanced use cases only. You usually only need to use `temperature`.
`top_p`	number	No	—	Use nucleus sampling. In nucleus sampling, we compute the cumulative distribution over all the options for each subsequent token in decreasing probability order and cut it off once it reaches a particular probability specified by `top_p`. You should either alter `temperature` or `top_p`, but not both. Recommended for advanced use cases only. You usually only need to use `temperature`.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "cache_control": {
      "type": "object",
      "description": "Top-level cache control automatically applies a cache_control marker to the last cacheable block in the request.",
      "properties": {
        "ttl": {
          "type": "string",
          "description": "The time-to-live for the cache control breakpoint.  This may be one the following values: - `5m`: 5 minutes - `1h`: 1 hour  Defaults to `5m`."
        },
        "type": {
          "type": "string",
          "description": "The type value"
        }
      },
      "required": [
        "type"
      ]
    },
    "container": {
      "type": "string",
      "description": "Container identifier for reuse across requests."
    },
    "inference_geo": {
      "type": "string",
      "description": "Specifies the geographic region for inference processing. If not specified, the workspace's `default_inference_geo` is used."
    },
    "max_tokens": {
      "type": "integer",
      "description": "The maximum number of tokens to generate before stopping.  Note that our models may stop _before_ reaching this maximum. This parameter only specifies the absolute maximum number of tokens to generate.  Different models have different maximum values for this parameter.  See [models](https://docs.claude.com/en/docs/models-overview) for details."
    },
    "messages": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "Input messages.  Our models are trained to operate on alternating `user` and `assistant` conversational turns. When creating a new `Message`, you specify the prior conversational turns with the `messages` parameter, and the model then generates the next `Message` in the conversation. Consecutive `user` or `assistant` turns in your request will be combined into a single turn.  Each input message must be an object with a `role` and `content`. You can specify a single `user`-role message, or you can include multiple `user` and `assistant` messages.  If the final message uses the `assistant` role, the response content will continue immediately from the content in that message. This can be used to constrain part of the model's response.  Example with a single `user` message:  ```json [{\"role\": \"user\", \"content\": \"Hello, Claude\"}] ```  Example with multiple conversational turns:  ```json [   {\"role\": \"user\", \"content\": \"Hello there.\"},   {\"role\": \"assistant\", \"content\": \"Hi, I'm Claude. How can I help you?\"},   {\"role\": \"user\", \"content\": \"Can you explain LLMs in plain English?\"}, ] ```  Example with a partially-filled response from Claude:  ```json [   {\"role\": \"user\", \"content\": \"What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun\"},   {\"role\": \"assistant\", \"content\": \"The best answer is (\"}, ] ```  Each input message `content` may be either a single `string` or an array of content blocks, where each block has a specific `type`. Using a `string` for `content` is shorthand for an array of one content block of type `\"text\"`. The following input messages are equivalent:  ```json {\"role\": \"user\", \"content\": \"Hello, Claude\"} ```  ```json {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"Hello, Claude\"}]} ```  See [input examples](https://docs.claude.com/en/api/messages-examples).  Note that if you want to include a [system prompt](https://docs.claude.com/en/docs/system-prompts), you can use the top-level `system` parameter — there is no `\"system\"` role for input messages in the Messages API.  There is a limit of 100,000 messages in a single request."
    },
    "metadata": {
      "type": "object",
      "description": "The metadata value",
      "properties": {
        "user_id": {
          "type": "string",
          "description": "An external identifier for the user who is associated with the request.  This should be a uuid, hash value, or other opaque identifier. Anthropic may use this id to help detect abuse. Do not include any identifying information such as name, email address, or phone number."
        }
      }
    },
    "model": {
      "description": "The model that will complete your prompt.\\n\\nSee [models](https://docs.anthropic.com/en/docs/models-overview) for additional details and options."
    },
    "output_config": {
      "type": "object",
      "description": "Output Config",
      "properties": {
        "effort": {
          "type": "string",
          "description": "How much effort the model should put into its response. Higher effort levels may result in more thorough analysis but take longer.  Valid values are `low`, `medium`, `high`, or `max`."
        },
        "format": {
          "type": "object",
          "description": "A schema to specify Claude's output format in responses. See [structured outputs](https://platform.claude.com/docs/en/build-with-claude/structured-outputs)"
        }
      }
    },
    "service_tier": {
      "type": "string",
      "description": "Determines whether to use priority capacity (if available) or standard capacity for this request.  Anthropic offers different levels of service for your API requests. See [service-tiers](https://docs.claude.com/en/api/service-tiers) for details.",
      "enum": [
        "auto",
        "standard_only"
      ]
    },
    "stop_sequences": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Custom text sequences that will cause the model to stop generating.  Our models will normally stop when they have naturally completed their turn, which will result in a response `stop_reason` of `\"end_turn\"`.  If you want the model to stop generating when it encounters custom strings of text, you can use the `stop_sequences` parameter. If the model encounters one of the custom sequences, the response `stop_reason` value will be `\"stop_sequence\"` and the response `stop_sequence` value will contain the matched stop sequence."
    },
    "stream": {
      "type": "boolean",
      "description": "Whether to incrementally stream the response using server-sent events.  See [streaming](https://docs.claude.com/en/api/messages-streaming) for details."
    },
    "system": {
      "description": "System prompt.  A system prompt is a way of providing context and instructions to Claude, such as specifying a particular goal or role. See our [guide to system prompts](https://docs.claude.com/en/docs/system-prompts)."
    },
    "temperature": {
      "type": "number",
      "description": "Amount of randomness injected into the response.  Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0` for analytical / multiple choice, and closer to `1.0` for creative and generative tasks.  Note that even with `temperature` of `0.0`, the results will not be fully deterministic."
    },
    "thinking": {
      "description": "Configuration for enabling Claude's extended thinking.  When enabled, responses include `thinking` content blocks showing Claude's thinking process before the final answer. Requires a minimum budget of 1,024 tokens and counts towards your `max_tokens` limit.  See [extended thinking](https://docs.claude.com/en/docs/build-with-claude/extended-thinking) for details."
    },
    "tool_choice": {
      "description": "How the model should use the provided tools. The model can use a specific tool, any available tool, decide by itself, or not use tools at all."
    },
    "tools": {
      "type": "array",
      "description": "Definitions of tools that the model may use.  If you include `tools` in your API request, the model may return `tool_use` content blocks that represent the model's use of those tools. You can then run those tools using the tool input generated by the model and then optionally return results back to the model using `tool_result` content blocks.  There are two types of tools: **client tools** and **server tools**. The behavior described below applies to client tools. For [server tools](https://docs.claude.com/en/docs/agents-and-tools/tool-use/overview\\#server-tools), see their individual documentation as each has its own behavior (e.g., the [web search tool](https://docs.claude.com/en/docs/agents-and-tools/tool-use/web-search-tool)).  Each tool definition includes:  * `name`: Name of the tool. * `description`: Optional, but strongly-recommended description of the tool. * `input_schema`: [JSON schema](https://json-schema.org/draft/2020-12) for the tool `input` shape that the model will produce in `tool_use` output content blocks.  For example, if you defined `tools` as:  ```json [   {     \"name\": \"get_stock_price\",     \"description\": \"Get the current stock price for a given ticker symbol.\",     \"input_schema\": {       \"type\": \"object\",       \"properties\": {         \"ticker\": {           \"type\": \"string\",           \"description\": \"The stock ticker symbol, e.g. AAPL for Apple Inc.\"         }       },       \"required\": [\"ticker\"]     }   } ] ```  And then asked the model \"What's the S&P 500 at today?\", the model might produce `tool_use` content blocks in the response like this:  ```json [   {     \"type\": \"tool_use\",     \"id\": \"toolu_01D7FLrfh4GYq7yT1ULFeyMV\",     \"name\": \"get_stock_price\",     \"input\": { \"ticker\": \"^GSPC\" }   } ] ```  You might then run your `get_stock_price` tool with `{\"ticker\": \"^GSPC\"}` as an input, and return the following back to the model in a subsequent `user` message:  ```json [   {     \"type\": \"tool_result\",     \"tool_use_id\": \"toolu_01D7FLrfh4GYq7yT1ULFeyMV\",     \"content\": \"259.75 USD\"   } ] ```  Tools can be used for workflows that include running client-side tools and functions, or more generally whenever you want the model to produce a particular JSON structure of output.  See our [guide](https://docs.claude.com/en/docs/tool-use) for more details."
    },
    "top_k": {
      "type": "integer",
      "description": "Only sample from the top K options for each subsequent token.  Used to remove \"long tail\" low probability responses. [Learn more technical details here](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277).  Recommended for advanced use cases only. You usually only need to use `temperature`."
    },
    "top_p": {
      "type": "number",
      "description": "Use nucleus sampling.  In nucleus sampling, we compute the cumulative distribution over all the options for each subsequent token in decreasing probability order and cut it off once it reaches a particular probability specified by `top_p`. You should either alter `temperature` or `top_p`, but not both.  Recommended for advanced use cases only. You usually only need to use `temperature`."
    }
  },
  "required": [
    "PCID",
    "max_tokens",
    "messages",
    "model"
  ]
}

anthropic_create_message_batch

Create a Message Batch Parameters:

Parameter	Type	Required	Default	Description
`requests`	object[]	Yes	—	List of requests for prompt completion. Each is an individual request to create a Message.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "requests": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "List of requests for prompt completion. Each is an individual request to create a Message."
    }
  },
  "required": [
    "PCID",
    "requests"
  ]
}

anthropic_create_skill

Create Skill Parameters:

Parameter	Type	Required	Default	Description
`display_title`	string	No	—	Display title for the skill. This is a human-readable label that is not included in the prompt sent to the model.
`files`	string[]	No	—	Files to upload for the skill. All files must be in the same top-level directory and must include a SKILL.md file at the root of that directory.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "display_title": {
      "type": "string",
      "description": "Display title for the skill.  This is a human-readable label that is not included in the prompt sent to the model."
    },
    "files": {
      "type": "array",
      "items": {
        "type": "string",
        "format": "binary"
      },
      "description": "Files to upload for the skill.  All files must be in the same top-level directory and must include a SKILL.md file at the root of that directory."
    }
  },
  "required": [
    "PCID"
  ]
}

anthropic_create_skill_version

Create Skill Version Parameters:

Parameter	Type	Required	Default	Description
`skill_id`	string	Yes	—	Unique identifier for the skill. The format and length of IDs may change over time.
`files`	string[]	No	—	Files to upload for the skill. All files must be in the same top-level directory and must include a SKILL.md file at the root of that directory.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "skill_id": {
      "type": "string",
      "description": "Unique identifier for the skill.  The format and length of IDs may change over time."
    },
    "files": {
      "type": "array",
      "items": {
        "type": "string",
        "format": "binary"
      },
      "description": "Files to upload for the skill.  All files must be in the same top-level directory and must include a SKILL.md file at the root of that directory."
    }
  },
  "required": [
    "PCID",
    "skill_id"
  ]
}

anthropic_delete_file

Delete File Parameters:

Parameter	Type	Required	Default	Description
`file_id`	string	Yes	—	ID of the File.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "file_id": {
      "type": "string",
      "description": "ID of the File."
    }
  },
  "required": [
    "PCID",
    "file_id"
  ]
}

anthropic_delete_message_batch

Delete a Message Batch Parameters:

Parameter	Type	Required	Default	Description
`message_batch_id`	string	Yes	—	ID of the Message Batch.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "message_batch_id": {
      "type": "string",
      "description": "ID of the Message Batch."
    }
  },
  "required": [
    "PCID",
    "message_batch_id"
  ]
}

anthropic_delete_skill

Delete Skill Parameters:

Parameter	Type	Required	Default	Description
`skill_id`	string	Yes	—	Unique identifier for the skill. The format and length of IDs may change over time.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "skill_id": {
      "type": "string",
      "description": "Unique identifier for the skill.  The format and length of IDs may change over time."
    }
  },
  "required": [
    "PCID",
    "skill_id"
  ]
}

anthropic_delete_skill_version

Delete Skill Version Parameters:

Parameter	Type	Required	Default	Description
`skill_id`	string	Yes	—	Unique identifier for the skill. The format and length of IDs may change over time.
`version`	string	Yes	—	Version identifier for the skill. Each version is identified by a Unix epoch timestamp (e.g., “1759178010641129”).

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "skill_id": {
      "type": "string",
      "description": "Unique identifier for the skill.  The format and length of IDs may change over time."
    },
    "version": {
      "type": "string",
      "description": "Version identifier for the skill.  Each version is identified by a Unix epoch timestamp (e.g., \"1759178010641129\")."
    }
  },
  "required": [
    "PCID",
    "skill_id",
    "version"
  ]
}

anthropic_download_file

Download File Parameters:

Parameter	Type	Required	Default	Description
`file_id`	string	Yes	—	ID of the File.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "file_id": {
      "type": "string",
      "description": "ID of the File."
    }
  },
  "required": [
    "PCID",
    "file_id"
  ]
}

anthropic_get_file

Get File Metadata Parameters:

Parameter	Type	Required	Default	Description
`file_id`	string	Yes	—	ID of the File.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "file_id": {
      "type": "string",
      "description": "ID of the File."
    }
  },
  "required": [
    "PCID",
    "file_id"
  ]
}

anthropic_get_message_batch

Retrieve a Message Batch Parameters:

Parameter	Type	Required	Default	Description
`message_batch_id`	string	Yes	—	ID of the Message Batch.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "message_batch_id": {
      "type": "string",
      "description": "ID of the Message Batch."
    }
  },
  "required": [
    "PCID",
    "message_batch_id"
  ]
}

anthropic_get_message_batch_results

Retrieve Message Batch results Parameters:

Parameter	Type	Required	Default	Description
`message_batch_id`	string	Yes	—	ID of the Message Batch.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "message_batch_id": {
      "type": "string",
      "description": "ID of the Message Batch."
    }
  },
  "required": [
    "PCID",
    "message_batch_id"
  ]
}

anthropic_get_model

Get a Model Parameters:

Parameter	Type	Required	Default	Description
`model_id`	string	Yes	—	Model identifier or alias.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "model_id": {
      "type": "string",
      "description": "Model identifier or alias."
    }
  },
  "required": [
    "PCID",
    "model_id"
  ]
}

anthropic_get_skill

Get Skill Parameters:

Parameter	Type	Required	Default	Description
`skill_id`	string	Yes	—	Unique identifier for the skill. The format and length of IDs may change over time.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "skill_id": {
      "type": "string",
      "description": "Unique identifier for the skill.  The format and length of IDs may change over time."
    }
  },
  "required": [
    "PCID",
    "skill_id"
  ]
}

anthropic_get_skill_version

Get Skill Version Parameters:

Parameter	Type	Required	Default	Description
`skill_id`	string	Yes	—	Unique identifier for the skill. The format and length of IDs may change over time.
`version`	string	Yes	—	Version identifier for the skill. Each version is identified by a Unix epoch timestamp (e.g., “1759178010641129”).

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "skill_id": {
      "type": "string",
      "description": "Unique identifier for the skill.  The format and length of IDs may change over time."
    },
    "version": {
      "type": "string",
      "description": "Version identifier for the skill.  Each version is identified by a Unix epoch timestamp (e.g., \"1759178010641129\")."
    }
  },
  "required": [
    "PCID",
    "skill_id",
    "version"
  ]
}

anthropic_list_files

List Files Parameters:

Parameter	Type	Required	Default	Description
`before_id`	string	No	—	ID of the object to use as a cursor for pagination. When provided, returns the page of results immediately before this object.
`after_id`	string	No	—	ID of the object to use as a cursor for pagination. When provided, returns the page of results immediately after this object.
`limit`	integer	No	—	Number of items to return per page. Defaults to `20`. Ranges from `1` to `1000`.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "before_id": {
      "type": "string",
      "description": "ID of the object to use as a cursor for pagination. When provided, returns the page of results immediately before this object."
    },
    "after_id": {
      "type": "string",
      "description": "ID of the object to use as a cursor for pagination. When provided, returns the page of results immediately after this object."
    },
    "limit": {
      "type": "integer",
      "description": "Number of items to return per page.  Defaults to `20`. Ranges from `1` to `1000`."
    }
  },
  "required": [
    "PCID"
  ]
}

anthropic_list_message_batches

List Message Batches Parameters:

Parameter	Type	Required	Default	Description
`before_id`	string	No	—	ID of the object to use as a cursor for pagination. When provided, returns the page of results immediately before this object.
`after_id`	string	No	—	ID of the object to use as a cursor for pagination. When provided, returns the page of results immediately after this object.
`limit`	integer	No	—	Number of items to return per page. Defaults to `20`. Ranges from `1` to `1000`.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "before_id": {
      "type": "string",
      "description": "ID of the object to use as a cursor for pagination. When provided, returns the page of results immediately before this object."
    },
    "after_id": {
      "type": "string",
      "description": "ID of the object to use as a cursor for pagination. When provided, returns the page of results immediately after this object."
    },
    "limit": {
      "type": "integer",
      "description": "Number of items to return per page.  Defaults to `20`. Ranges from `1` to `1000`."
    }
  },
  "required": [
    "PCID"
  ]
}

anthropic_list_models

List Models Parameters:

Parameter	Type	Required	Default	Description
`before_id`	string	No	—	ID of the object to use as a cursor for pagination. When provided, returns the page of results immediately before this object.
`after_id`	string	No	—	ID of the object to use as a cursor for pagination. When provided, returns the page of results immediately after this object.
`limit`	integer	No	—	Number of items to return per page. Defaults to `20`. Ranges from `1` to `1000`.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "before_id": {
      "type": "string",
      "description": "ID of the object to use as a cursor for pagination. When provided, returns the page of results immediately before this object."
    },
    "after_id": {
      "type": "string",
      "description": "ID of the object to use as a cursor for pagination. When provided, returns the page of results immediately after this object."
    },
    "limit": {
      "type": "integer",
      "description": "Number of items to return per page.  Defaults to `20`. Ranges from `1` to `1000`."
    }
  },
  "required": [
    "PCID"
  ]
}

anthropic_list_skill_versions

List Skill Versions Parameters:

Parameter	Type	Required	Default	Description
`skill_id`	string	Yes	—	Unique identifier for the skill. The format and length of IDs may change over time.
`page`	string	No	—	Optionally set to the `next_page` token from the previous response.
`limit`	integer	No	—	Number of items to return per page. Defaults to `20`. Ranges from `1` to `1000`.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "skill_id": {
      "type": "string",
      "description": "Unique identifier for the skill.  The format and length of IDs may change over time."
    },
    "page": {
      "type": "string",
      "description": "Optionally set to the `next_page` token from the previous response."
    },
    "limit": {
      "type": "integer",
      "description": "Number of items to return per page.  Defaults to `20`. Ranges from `1` to `1000`."
    }
  },
  "required": [
    "PCID",
    "skill_id"
  ]
}

anthropic_list_skills

List Skills Parameters:

Parameter	Type	Required	Default	Description
`page`	string	No	—	Pagination token for fetching a specific page of results. Pass the value from a previous response’s `next_page` field to get the next page of results.
`limit`	integer	No	—	Number of results to return per page. Maximum value is 100. Defaults to 20.
`source`	string	No	—	Filter skills by source. If provided, only skills from the specified source will be returned: * `"custom"`: only return user-created skills * `"anthropic"`: only return Anthropic-created skills

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "page": {
      "type": "string",
      "description": "Pagination token for fetching a specific page of results.  Pass the value from a previous response's `next_page` field to get the next page of results."
    },
    "limit": {
      "type": "integer",
      "description": "Number of results to return per page.  Maximum value is 100. Defaults to 20."
    },
    "source": {
      "type": "string",
      "description": "Filter skills by source.  If provided, only skills from the specified source will be returned: * `\"custom\"`: only return user-created skills * `\"anthropic\"`: only return Anthropic-created skills"
    }
  },
  "required": [
    "PCID"
  ]
}

anthropic_upload_file

Upload File Parameters:

Parameter	Type	Required	Default	Description
`file`	string	Yes	—	The file to upload

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "file": {
      "type": "string",
      "description": "The file to upload"
    }
  },
  "required": [
    "PCID",
    "file"
  ]
}

Triggers API

Platform API

Embedded MCP Servers

Application MCP Servers

Skills Guides

Tools

anthropic_analyze_image

anthropic_batch_messages

anthropic_cancel_message_batch

anthropic_count_tokens

anthropic_create_message

anthropic_create_message_batch

anthropic_create_skill

anthropic_create_skill_version

anthropic_delete_file

anthropic_delete_message_batch

anthropic_delete_skill

anthropic_delete_skill_version

anthropic_download_file

anthropic_get_file

anthropic_get_message_batch

anthropic_get_message_batch_results

anthropic_get_model

anthropic_get_skill

anthropic_get_skill_version

anthropic_list_files

anthropic_list_message_batches

anthropic_list_models

anthropic_list_skill_versions

anthropic_list_skills

anthropic_upload_file

Triggers API

Platform API

Embedded MCP Servers

Application MCP Servers

Skills Guides

Documentation Index

​Tools

​anthropic_analyze_image

​anthropic_batch_messages

​anthropic_cancel_message_batch

​anthropic_count_tokens

​anthropic_create_message

​anthropic_create_message_batch

​anthropic_create_skill

​anthropic_create_skill_version

​anthropic_delete_file

​anthropic_delete_message_batch

​anthropic_delete_skill

​anthropic_delete_skill_version

​anthropic_download_file

​anthropic_get_file

​anthropic_get_message_batch

​anthropic_get_message_batch_results

​anthropic_get_model

​anthropic_get_skill

​anthropic_get_skill_version

​anthropic_list_files

​anthropic_list_message_batches

​anthropic_list_models

​anthropic_list_skill_versions

​anthropic_list_skills

​anthropic_upload_file

Tools

anthropic_analyze_image

anthropic_batch_messages

anthropic_cancel_message_batch

anthropic_count_tokens

anthropic_create_message

anthropic_create_message_batch

anthropic_create_skill

anthropic_create_skill_version

anthropic_delete_file

anthropic_delete_message_batch

anthropic_delete_skill

anthropic_delete_skill_version

anthropic_download_file

anthropic_get_file

anthropic_get_message_batch

anthropic_get_message_batch_results

anthropic_get_model

anthropic_get_skill

anthropic_get_skill_version

anthropic_list_files

anthropic_list_message_batches

anthropic_list_models

anthropic_list_skill_versions

anthropic_list_skills

anthropic_upload_file