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testmo_append_automation_run

Append test artifacts, custom fields, or links to an existing automation run to add or update test data.

Instructions

Append test artifacts, fields, or links to an existing automation run.

Args: automation_run_id: The automation run ID. artifacts: External test artifacts to append. fields: Custom fields to append. links: Links to append.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
automation_run_idYes
artifactsNo
fieldsNo
linksNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main tool handler for testmo_append_automation_run. An async function decorated with @mcp.tool() that accepts an automation_run_id and optional artifacts, fields, and links. It builds a data dict and sends a POST request to /automation/runs/{automation_run_id}/append.
    @mcp.tool()
    async def testmo_append_automation_run(
        automation_run_id: int,
        artifacts: list[dict[str, Any]] | None = None,
        fields: list[dict[str, Any]] | None = None,
        links: list[dict[str, Any]] | None = None,
    ) -> dict[str, Any]:
        """Append test artifacts, fields, or links to an existing automation run.
    
        Args:
            automation_run_id: The automation run ID.
            artifacts: External test artifacts to append.
            fields: Custom fields to append.
            links: Links to append.
        """
        data: dict[str, Any] = {}
        if artifacts:
            data["artifacts"] = artifacts
        if fields:
            data["fields"] = fields
        if links:
            data["links"] = links
        return await _request(
            "POST", f"/automation/runs/{automation_run_id}/append", data=data
        )
  • The tool is registered via the @mcp.tool() decorator on line 177 from the shared FastMCP instance (imported from ..server as mcp). The module is imported in testmo-mcp.py on line 17.
    @mcp.tool()
  • Input schema defined via type annotations: automation_run_id (int), artifacts (list[dict] | None), fields (list[dict] | None), links (list[dict] | None). Return type is dict[str, Any]. Docstring serves as the description for the AI.
    @mcp.tool()
    async def testmo_append_automation_run(
        automation_run_id: int,
        artifacts: list[dict[str, Any]] | None = None,
        fields: list[dict[str, Any]] | None = None,
        links: list[dict[str, Any]] | None = None,
    ) -> dict[str, Any]:
        """Append test artifacts, fields, or links to an existing automation run.
    
        Args:
            automation_run_id: The automation run ID.
            artifacts: External test artifacts to append.
            fields: Custom fields to append.
            links: Links to append.
        """
        data: dict[str, Any] = {}
        if artifacts:
            data["artifacts"] = artifacts
        if fields:
            data["fields"] = fields
        if links:
            data["links"] = links
        return await _request(
            "POST", f"/automation/runs/{automation_run_id}/append", data=data
        )
  • testmo-mcp.py:17-23 (registration)
    The top-level entry point that imports the automation module (which contains the tool). The import triggers the @mcp.tool() decorator to register all tools in that module on the FastMCP instance.
    import testmo.tools.automation  # noqa: F401
    import testmo.tools.issues  # noqa: F401
    import testmo.tools.composite  # noqa: F401
    import testmo.tools.utility  # noqa: F401
    
    if __name__ == "__main__":
        mcp.run(transport="stdio")
Behavior2/5

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

With no annotations, the description carries full burden. It only states the action without disclosing behavioral traits such as whether appending overwrites or adds, required permissions, error states, or idempotency.

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 concise: a single sentence summary followed by a bullet list of parameters. No unnecessary information.

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?

The description lacks mention of return values or error conditions, though an output schema exists. It is adequate for a simple append action but could be more comprehensive about behavior and prerequisites.

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?

Schema coverage is 0%, so the description must compensate. It provides minimal explanations for each parameter (e.g., 'The automation run ID', 'External test artifacts to append') but does not clarify the structure of the objects (e.g., required fields for artifacts).

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 ('Append') and the resource ('existing automation run') with specific items (artifacts, fields, links). It distinguishes from sibling tools like testmo_create_automation_run and testmo_complete_automation_run.

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?

The description implies usage for appending to an existing run but does not explicitly state when to use this tool over alternatives (e.g., testmo_append_automation_run_thread) or mention prerequisites like the run must exist.

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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