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testmo_get_milestone

Retrieve details of a milestone by its ID. Optionally expand related entities for more context.

Instructions

Get details of a specific milestone by ID.

Args: milestone_id: The milestone ID. expands: Related entities to include.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
milestone_idYes
expandsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The tool handler function that executes the testmo_get_milestone logic. Decorated with @mcp.tool(), it takes milestone_id and optional expands, makes a GET request to /milestones/{milestone_id}, and returns the result.
    @mcp.tool()
    async def testmo_get_milestone(
        milestone_id: int,
        expands: list[str] | None = None,
    ) -> dict[str, Any]:
        """Get details of a specific milestone by ID.
    
        Args:
            milestone_id: The milestone ID.
            expands: Related entities to include.
        """
        params: dict[str, Any] = {}
        if expands:
            params["expands"] = ",".join(expands)
        result = await _request(
            "GET", f"/milestones/{milestone_id}", params=params if params else None
        )
        return result.get("result", result)
  • The tool is registered via the @mcp.tool() decorator on the async function, which binds it to the FastMCP server instance.
    @mcp.tool()
    async def testmo_get_milestone(
  • testmo-mcp.py:13-13 (registration)
    The import of testmo.tools.milestones in the main entrypoint triggers the @mcp.tool() decorator, registering the tool on the MCP server.
    import testmo.tools.milestones  # noqa: F401
  • The _request helper function used by the handler to make HTTP requests to the Testmo API.
    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 function signature defines the input schema: milestone_id (int, required) and expands (optional list of strings). Returns a dict.
    async def testmo_get_milestone(
        milestone_id: int,
        expands: list[str] | None = None,
    ) -> dict[str, Any]:
Behavior2/5

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

No annotations are provided, so the description carries full burden. It only states the basic purpose; it does not disclose read-only behavior, error handling (e.g., if ID not found), or any side effects.

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 very short, front-loaded with the main action, and contains no unnecessary words.

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

Completeness3/5

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

With an output schema present, the tool does not need to describe return values. However, it lacks contextual details like differentiation from list_milestones or typical usage patterns, which would improve completeness.

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?

The description adds brief explanations for both parameters: 'milestone_id: The milestone ID' and 'expands: Related entities to include'. Given 0% schema description coverage, this adds value, but 'expands' could specify which entities are expandable.

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 action ('Get details') and the resource ('a specific milestone by ID'). It distinguishes itself from sibling 'testmo_list_milestones', which lists all milestones, implying this is for a single entity.

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?

No explicit guidance on when to use this tool versus alternatives like 'testmo_list_milestones'. It does not mention prerequisites or 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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