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research_list

List research requests from Exa with pagination support. Use cursor and limit to control results.

Instructions

List research requests using Exa.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cursorNoPagination cursor from a previous response.
limitNoMaximum number of results to return.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The research_list tool handler function, registered via @mcp.tool() decorator. It accepts optional cursor and limit parameters, builds an arguments dict, and delegates to the remote Exa MCP server by calling _call_mcp_tool with the tool name 'exa_research_list'.
    @mcp.tool()
    def research_list(
        cursor: str | None = None,
        limit: int | None = None,
    ) -> dict[str, Any]:
        """List research requests using Exa.
    
        Args:
            cursor: Pagination cursor from a previous response.
            limit: Maximum number of results to return.
    
        Returns:
            Dict containing the list of research requests.
    
        Example:
            >>> research_list(limit=10)
            {"data": [...], "has_more": true, "next_cursor": "..."}
        """
        import asyncio
    
        arguments: dict[str, Any] = {}
        if cursor is not None:
            arguments["cursor"] = cursor
        if limit is not None:
            arguments["limit"] = limit
    
        try:
            result = asyncio.get_event_loop().run_until_complete(
                _call_mcp_tool("exa_research_list", arguments)
            )
            return result
        except Exception as e:
            return {"error": str(e)}
  • The tool is registered as an MCP tool via the @mcp.tool() decorator on the FastMCP instance named 'mcp-exa'. The mcp instance is created at line 10 and exported via __init__.py.
    @mcp.tool()
  • Helper function _call_mcp_tool that sends a JSON-RPC request to the public Exa MCP server. It is called by research_list with the tool name 'exa_research_list' and the arguments dict.
    async def _call_mcp_tool(tool_name: str, arguments: dict[str, Any]) -> dict[str, Any]:
        """Call a tool on the public Exa MCP server."""
        request = {
            "jsonrpc": "2.0",
            "id": 1,
            "method": "tools/call",
            "params": {
                "name": tool_name,
                "arguments": arguments,
            },
        }
    
        async with httpx.AsyncClient(timeout=60.0) as client:
            response = await client.post(
                f"{BASE_URL}/mcp",
                json=request,
                headers={
                    "accept": "application/json, text/event-stream",
                    "content-type": "application/json",
                },
            )
            response.raise_for_status()
            response_text = response.text
    
            lines = response_text.split("\n")
            for line in lines:
                if line.startswith("data: "):
                    data = line[6:]
                    result = {"jsonrpc": "2.0", "id": 1, "result": {}}
                    try:
                        parsed = eval(data)
                    except Exception:
                        pass
                    else:
                        if "result" in parsed and parsed["result"].get("content"):
                            return {
                                "results": parsed["result"]["content"][0].get("text", "")
                            }
    
            return {"results": ""}
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description alone must disclose behavior. It fails to mention that results are paginated (despite cursor/limit parameters), whether it is read-only, or any side effects. The lack of behavioral detail is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise (5 words), which sacrifices necessary details. While not verbose, it under-specifies the tool's capabilities and usage, making it less useful than a moderately longer description would be.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the existence of an output schema, return values need not be explained. However, pagination behavior (default limit, cursor usage) is entirely omitted, and no context is provided about the scope of listed requests (e.g., all by user or workspace). This makes the description incomplete for a list tool with pagination.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the input schema already documents cursor and limit fully. The description adds no extra meaning beyond 'List research requests using Exa,' which does not explain how parameters affect results. Baseline 3 is appropriate as the schema bears the burden.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it 'List[s] research requests using Exa,' indicating a listing operation on a specific resource. It effectively distinguishes from siblings like 'research_create' and 'research_get' by using the verb 'list.' However, the term 'research requests' is ambiguous without further context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives (e.g., research_get for a single request, research_poll for polling status). It does not mention prerequisites or limitations, leaving the agent without context to choose appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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