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testmo_get_issue_connection

Fetch details of a specific issue connection by its ID, with optional expanded related entities.

Instructions

Get details of a specific issue connection.

Args: connection_id: The issue connection ID. expands: Related entities to include.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
connection_idYes
expandsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for the testmo_get_issue_connection tool. Makes a GET request to /issues/connections/{connection_id} to retrieve details of a specific issue connection.
    @mcp.tool()
    async def testmo_get_issue_connection(
        connection_id: int,
        expands: list[str] | None = None,
    ) -> dict[str, Any]:
        """Get details of a specific issue connection.
    
        Args:
            connection_id: The issue connection ID.
            expands: Related entities to include.
        """
        params: dict[str, Any] = {}
        if expands:
            params["expands"] = ",".join(expands)
        result = await _request(
            "GET",
            f"/issues/connections/{connection_id}",
            params=params if params else None,
        )
        return result.get("result", result)
  • Input parameters for the tool: connection_id (int, required) and expands (list[str], optional).
    connection_id: int,
    expands: list[str] | None = None,
  • Registered as an MCP tool via the @mcp.tool() decorator on the FastMCP instance from testmo/server.py.
    @mcp.tool()
    async def testmo_get_issue_connection(
  • testmo-mcp.py:18-18 (registration)
    Top-level entry point imports testmo.tools.issues to trigger registration of all issue tools (including testmo_get_issue_connection) on the mcp instance.
    import testmo.tools.issues  # noqa: F401
  • The _request helper function used to make the HTTP GET request 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()
Behavior2/5

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

With no annotations, the description carries full burden for behavioral transparency. It only states the action without disclosing side effects, permissions, error handling, or any constraints. The tool is read-only by inference, but this is not explicit.

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 extremely concise with two sentences and a parameter list. Every line is necessary and front-loaded. No wasted words.

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 low complexity (2 parameters, output schema exists), the description omits any guidance on when to use, what to expect in output, or error scenarios. It lacks context for an AI agent to confidently invoke the tool.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It adds minimal meaning: 'connection_id: The issue connection ID' repeats the schema, and 'expands: Related entities to include' is vague. No details on allowed expand values or format.

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 the tool's purpose: 'Get details of a specific issue connection.' It uses a specific verb and resource, distinguishing it from sibling tools like testmo_list_issue_connections, though it does not explicitly contrast them.

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 usage guidelines are provided. The description does not indicate when to use this tool over alternatives, nor does it specify prerequisites or exclusions.

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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