ask_perplexity
Retrieve source-backed information and contextual data from the internet for research, fact-checking, or informed decision-making, with citations and diverse response choices included.
Instructions
Perplexity equips agents with a specialized tool for efficiently gathering source-backed information from the internet, ideal for scenarios requiring research, fact-checking, or contextual data to inform decisions and responses. Each response includes citations, which provide transparent references to the sources used for the generated answer, and choices, which contain the model's suggested responses, enabling users to access reliable information and diverse perspectives. This function may encounter timeout errors due to long processing times, but retrying the operation can lead to successful completion. [Response structure]
id: An ID generated uniquely for each response.
model: The model used to generate the response.
object: The object type, which always equals
chat.completion.created: The Unix timestamp (in seconds) of when the completion was created.
citations[]: Citations for the generated answer.
choices[]: The list of completion choices the model generated for the input prompt.
usage: Usage statistics for the completion request.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| messages | Yes | A list of messages comprising the conversation so far. | |
| model | Yes | The name of the model that will complete your prompt. |
Implementation Reference
- The handler function for calling the 'ask_perplexity' tool, which makes an HTTP POST request to the Perplexity API with the provided arguments and returns the response as text content.@server.call_tool() async def handle_call_tool( name: str, arguments: dict ) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]: if name != "ask_perplexity": raise ValueError(f"Unknown tool: {name}") try: async with httpx.AsyncClient() as client: response = await client.post( f"{PERPLEXITY_API_BASE_URL}/chat/completions", headers={ "Authorization": f"Bearer {PERPLEXITY_API_KEY}", "Content-Type": "application/json", }, json=arguments, timeout=None, ) response.raise_for_status() except httpx.HTTPError as e: raise RuntimeError(f"API error: {str(e)}") return [ types.TextContent( type="text", text=response.text, ) ]
- src/mcp_server_perplexity/server.py:17-83 (registration)The tool registration via list_tools handler, defining the 'ask_perplexity' tool with name, description, and input schema.@server.list_tools() async def handle_list_tools() -> list[types.Tool]: return [ types.Tool( name="ask_perplexity", description=dedent( """ Perplexity equips agents with a specialized tool for efficiently gathering source-backed information from the internet, ideal for scenarios requiring research, fact-checking, or contextual data to inform decisions and responses. Each response includes citations, which provide transparent references to the sources used for the generated answer, and choices, which contain the model's suggested responses, enabling users to access reliable information and diverse perspectives. This function may encounter timeout errors due to long processing times, but retrying the operation can lead to successful completion. [Response structure] - id: An ID generated uniquely for each response. - model: The model used to generate the response. - object: The object type, which always equals `chat.completion`. - created: The Unix timestamp (in seconds) of when the completion was created. - citations[]: Citations for the generated answer. - choices[]: The list of completion choices the model generated for the input prompt. - usage: Usage statistics for the completion request. """ ), inputSchema={ "type": "object", "properties": { "model": { "type": "string", "description": "The name of the model that will complete your prompt.", "enum": [ "llama-3.1-sonar-small-128k-online", # Commenting out larger models,which have higher risks of timing out, # until Claude Desktop can handle long-running tasks effectively. # "llama-3.1-sonar-large-128k-online", # "llama-3.1-sonar-huge-128k-online", ], }, "messages": { "type": "array", "description": "A list of messages comprising the conversation so far.", "items": { "type": "object", "properties": { "content": { "type": "string", "description": "The contents of the message in this turn of conversation.", }, "role": { "type": "string", "description": "The role of the speaker in this turn of conversation. After the (optional) system message, user and assistant roles should alternate with user then assistant, ending in user.", "enum": ["system", "user", "assistant"], }, }, "required": ["content", "role"], }, }, }, "required": ["model", "messages"], }, ) ]
- The input schema defining the parameters for the 'ask_perplexity' tool: model (enum) and messages (array of role/content).inputSchema={ "type": "object", "properties": { "model": { "type": "string", "description": "The name of the model that will complete your prompt.", "enum": [ "llama-3.1-sonar-small-128k-online", # Commenting out larger models,which have higher risks of timing out, # until Claude Desktop can handle long-running tasks effectively. # "llama-3.1-sonar-large-128k-online", # "llama-3.1-sonar-huge-128k-online", ], }, "messages": { "type": "array", "description": "A list of messages comprising the conversation so far.", "items": { "type": "object", "properties": { "content": { "type": "string", "description": "The contents of the message in this turn of conversation.", }, "role": { "type": "string", "description": "The role of the speaker in this turn of conversation. After the (optional) system message, user and assistant roles should alternate with user then assistant, ending in user.", "enum": ["system", "user", "assistant"], }, }, "required": ["content", "role"], }, }, }, "required": ["model", "messages"], },