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martinfrasch

ResearchTwin

list_researchers

Browse registered researchers on the ResearchTwin platform to access their profiles, publications, datasets, and repositories for comprehensive academic exploration.

Instructions

List all researchers registered on the ResearchTwin platform.

Returns researcher slugs and display names. Use a slug with other tools to explore a specific researcher's profile, papers, datasets, and repositories.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The MCP tool handler that executes the list_researchers logic. Calls backend API via _get('/api/researchers'), formats and returns researcher data with slugs and display names.
    @mcp.tool(annotations=ToolAnnotations(title="List Researchers", read_only_hint=True))
    async def list_researchers() -> str:
        """List all researchers registered on the ResearchTwin platform.
    
        Returns researcher slugs and display names. Use a slug with other
        tools to explore a specific researcher's profile, papers, datasets,
        and repositories.
        """
        data = await _get("/api/researchers")
        researchers = data.get("researchers", [])
        if not researchers:
            return "No researchers registered yet."
        lines = [f"- **{r['display_name']}** (slug: `{r['slug']}`)" for r in researchers]
        return f"**{len(researchers)} researchers:**\n" + "\n".join(lines)
  • Helper function _get() that makes async HTTP requests to the ResearchTwin backend API. Used by list_researchers to fetch researcher data.
    async def _get(path: str, params: dict | None = None) -> dict:
        """Make a GET request to the ResearchTwin API."""
        async with httpx.AsyncClient(timeout=TIMEOUT) as client:
            resp = await client.get(f"{BASE_URL}{path}", params=params)
            resp.raise_for_status()
            return resp.json()
  • Backend FastAPI endpoint /api/researchers that provides the data source. Queries researcher slugs and returns list with slug and display_name for each researcher.
    @app.get("/api/researchers")
    def list_researchers():
        """Return available researchers with display names."""
        result = []
        for slug in researchers.list_slugs():
            r = researchers.get_researcher(slug)
            result.append({"slug": slug, "display_name": r["display_name"]})
        return {"researchers": result}
  • Pydantic response model defining the structure of researcher data with slug, display_name, tier, and message fields.
    class RegisterResponse(BaseModel):
        slug: str
        display_name: str
        tier: int
        message: str
Behavior4/5

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

With no annotations provided beyond a title, the description carries the full burden of behavioral disclosure. It effectively describes what the tool returns ('researcher slugs and display names') and how the output can be used with other tools. It doesn't mention limitations like pagination, rate limits, or permissions, but for a simple list operation with 0 parameters, this is reasonably complete.

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

Conciseness5/5

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

The description is perfectly concise and well-structured in two short paragraphs. The first sentence states the purpose, the second describes the return format, and the third provides usage guidance. Every sentence earns its place with no wasted words.

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

Completeness5/5

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

Given the tool's simplicity (0 parameters, no annotations beyond title, but with an output schema), the description is complete enough. It explains what the tool does, what it returns, and how to use the output. With an output schema handling return value documentation, the description doesn't need to detail the response structure further.

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

Parameters4/5

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

The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the lack of inputs. The description appropriately doesn't add parameter information, maintaining focus on the tool's purpose and output. A baseline of 4 is applied for tools with no parameters.

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

Purpose5/5

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

The description clearly states the verb ('List') and resource ('all researchers registered on the ResearchTwin platform'), making the purpose specific and unambiguous. It distinguishes this tool from siblings by focusing on listing researchers rather than getting specific resources like datasets, papers, or profiles.

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

Usage Guidelines4/5

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

The description provides clear context for when to use this tool ('to explore a specific researcher's profile, papers, datasets, and repositories') and mentions using the returned slugs with other tools. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools for different scenarios.

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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