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testmo_list_projects

List all accessible Testmo projects, returning their IDs, names, and metadata.

Instructions

List all accessible Testmo projects. Returns project IDs, names, and metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The actual handler function for the testmo_list_projects tool. It is decorated with @mcp.tool() and makes a GET request to /projects via the internal _request helper, returning the 'result' list.
    @mcp.tool()
    async def testmo_list_projects() -> list[dict[str, Any]]:
        """List all accessible Testmo projects. Returns project IDs, names, and metadata."""
        result = await _request("GET", "/projects")
        return result.get("result", [])
  • testmo/server.py:1-6 (registration)
    The MCP FastMCP instance is created here. The @mcp.tool() decorator on the handler registers the tool on this instance.
    from dotenv import load_dotenv
    from mcp.server.fastmcp import FastMCP
    
    load_dotenv()
    
    mcp = FastMCP("testmo-mcp")
  • testmo-mcp.py:8-11 (registration)
    The entry point imports testmo.tools.projects (and all other tool modules) to trigger side-effect registration via @mcp.tool() decorators.
    from testmo.server import mcp
    
    # Import tool modules to register all tools on the mcp instance
    import testmo.tools.projects  # noqa: F401
  • The _request helper function used by testmo_list_projects to make HTTP requests to the Testmo API. Handles auth, error responses, and JSON parsing.
    async def _request(
        method: str,
        endpoint: str,
        data: dict[str, Any] | None = None,
        params: dict[str, Any] | None = None,
    ) -> dict[str, Any]:
        async with _get_client() as client:
            response = await client.request(
                method=method,
                url=endpoint,
                json=data,
                params=params,
            )
            if response.status_code == 204:
                return {"success": True}
            if response.status_code >= 400:
                try:
                    error_body = response.json()
                except Exception:
                    error_body = response.text
                raise RuntimeError(
                    f"Testmo API error {response.status_code}: "
                    f"{json.dumps(error_body) if isinstance(error_body, dict) else error_body}"
                )
            return response.json()
  • The schema (signature and docstring) for testmo_list_projects: takes no arguments, returns list[dict[str, Any]].
    async def testmo_list_projects() -> list[dict[str, Any]]:
        """List all accessible Testmo projects. Returns project IDs, names, and metadata."""
        result = await _request("GET", "/projects")
        return result.get("result", [])
Behavior3/5

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

No annotations, but description provides return content (IDs, names, metadata). Lacks disclosure of authentication, rate limits, or pagination. Adequate for a simple read operation.

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?

Single sentence, front-loaded with core action, no wasted words. Efficient and clear.

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

Completeness4/5

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

Simple tool with no parameters and output schema exists, description covers return fields. Could mention if pagination is present, but likely not needed for a list-all.

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?

No parameters, so baseline is 4. Description adds no parameter info but schema coverage is 100% trivially.

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?

Clearly states it lists all accessible Testmo projects, returns IDs, names, metadata. Uses specific verb 'list' and resource 'projects', easily distinguished from sibling tools like testmo_get_project.

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

Usage Guidelines3/5

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

No explicit guidance on when to use or not, but given the simplicity (no parameters, list all), usage is implicit. Lacks mention of alternatives or context.

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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