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testmo_list_issue_connections

List configured issue tracker integrations and obtain IDs needed to connect issues to test cases.

Instructions

List available issue integrations (GitHub, Jira, Azure DevOps, etc.).

Discover configured issue tracker integrations. Returns integration_id and connection_project_id needed for linking issues to test cases.

Args: project_id: Filter by project ID (optional). integration_type: Filter by type (e.g., 'github', 'jira', 'azure_devops'). is_active: Filter by active status (optional). page: Page number (default: 1). per_page: Results per page (default: 100). Valid: 25, 50, 100. expands: Related entities to include.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idNo
integration_typeNo
is_activeNo
pageNo
per_pageNo
expandsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler function for testmo_list_issue_connections tool. Decorated with @mcp.tool(), it sends a GET request to /issues/connections with optional filters (project_id, integration_type, is_active) and pagination parameters (page, per_page). Returns list of issue tracker integrations.
    @mcp.tool()
    async def testmo_list_issue_connections(
        project_id: int | None = None,
        integration_type: str | None = None,
        is_active: bool | None = None,
        page: int = 1,
        per_page: int = 100,
        expands: list[str] | None = None,
    ) -> dict[str, Any]:
        """List available issue integrations (GitHub, Jira, Azure DevOps, etc.).
    
        Discover configured issue tracker integrations. Returns integration_id and
        connection_project_id needed for linking issues to test cases.
    
        Args:
            project_id: Filter by project ID (optional).
            integration_type: Filter by type (e.g., 'github', 'jira', 'azure_devops').
            is_active: Filter by active status (optional).
            page: Page number (default: 1).
            per_page: Results per page (default: 100). Valid: 25, 50, 100.
            expands: Related entities to include.
        """
        params: dict[str, Any] = {"page": page, "per_page": per_page}
        if project_id is not None:
            params["project_id"] = project_id
        if integration_type:
            params["integration_type"] = integration_type
        if is_active is not None:
            params["is_active"] = is_active
        if expands:
            params["expands"] = ",".join(expands)
        return await _request("GET", "/issues/connections", params=params)
  • Registration of the tool via the @mcp.tool() decorator on the FastMCP instance defined in testmo/server.py.
    @mcp.tool()
  • Helper function _request that makes HTTP requests to the Testmo API. Used by the handler to send GET /issues/connections.
    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()
  • Input schema/parameters for the tool: project_id (optional int), integration_type (optional str), is_active (optional bool), page (int, default 1), per_page (int, default 100), expands (optional list of strings). Returns dict[str, Any].
    @mcp.tool()
    async def testmo_list_issue_connections(
        project_id: int | None = None,
        integration_type: str | None = None,
        is_active: bool | None = None,
        page: int = 1,
        per_page: int = 100,
        expands: list[str] | None = None,
    ) -> dict[str, Any]:
        """List available issue integrations (GitHub, Jira, Azure DevOps, etc.).
    
        Discover configured issue tracker integrations. Returns integration_id and
        connection_project_id needed for linking issues to test cases.
    
        Args:
            project_id: Filter by project ID (optional).
            integration_type: Filter by type (e.g., 'github', 'jira', 'azure_devops').
            is_active: Filter by active status (optional).
            page: Page number (default: 1).
            per_page: Results per page (default: 100). Valid: 25, 50, 100.
            expands: Related entities to include.
        """
        params: dict[str, Any] = {"page": page, "per_page": per_page}
        if project_id is not None:
            params["project_id"] = project_id
        if integration_type:
            params["integration_type"] = integration_type
        if is_active is not None:
            params["is_active"] = is_active
        if expands:
            params["expands"] = ",".join(expands)
        return await _request("GET", "/issues/connections", params=params)
Behavior3/5

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

No annotations provided, so description must cover behavior. It describes the return fields (integration_id, connection_project_id) and lists parameters, but does not mention pagination details, auth requirements, or that it's a read-only operation beyond the nature of 'list'.

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 brief, front-loads the core purpose, and uses a structured Args list. Every sentence adds value without redundancy.

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?

An output schema exists (context signals), so return value details are not required. The description covers usage and all parameters. Minor omission is handling of empty results, but overall complete.

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

Parameters5/5

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

Despite schema coverage at 0%, the description includes a dedicated Args section that explains each parameter's purpose (e.g., integration_type for filtering by type, per_page valid values). This adds significant meaning beyond the schema.

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 explicitly states 'List available issue integrations' with examples (GitHub, Jira, Azure DevOps), clearly distinguishing it from the sibling 'testmo_get_issue_connection' which retrieves a single connection.

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 advises 'Discover configured issue tracker integrations' and mentions the output is needed for linking issues, implying a prerequisite. It lacks explicit 'when not to use' but context with siblings provides implicit guidance.

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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