Skip to main content
Glama

get_run_status

Check the status of a Grid run by providing its request ID. Poll every 2-5 seconds until the status is completed or failed.

Instructions

Check the status of a previously triggered Grid run.

Use this after run_grid returns a request_id (either from async mode or when sync mode times out after 120 seconds).

Poll every 2-5 seconds until status is "completed" or "failed". Avoid polling more frequently as requests count toward the rate limit (5 req/sec per workspace).

Args: request_id: The request_id UUID returned by run_grid.

Returns: {mode, status, grid_id, outputs (when completed)}. Status is one of: "running", "completed", or "failed". When completed, outputs contains {column_id: {value, name}} for each enriched column.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
request_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • main.py:237-260 (handler)
    The handler function for the get_run_status tool. It checks the status of a previously triggered Grid run by performing a GET request to /run/status/{request_id} and returning the JSON result.
    @mcp.tool()
    def get_run_status(request_id: str) -> str:
        """
        Check the status of a previously triggered Grid run.
    
        Use this after run_grid returns a request_id (either from async mode or
        when sync mode times out after 120 seconds).
    
        Poll every 2-5 seconds until status is "completed" or "failed".
        Avoid polling more frequently as requests count toward the rate limit
        (5 req/sec per workspace).
    
        Args:
            request_id: The request_id UUID returned by run_grid.
    
        Returns: {mode, status, grid_id, outputs (when completed)}.
        Status is one of: "running", "completed", or "failed".
        When completed, outputs contains {column_id: {value, name}} for each
        enriched column.
        """
        if not request_id:
            raise ValueError("request_id must not be empty")
        data = _get(f"/run/status/{request_id}")
        return json.dumps(data, indent=2)
  • main.py:237-237 (registration)
    Registration of the get_run_status tool using the @mcp.tool() decorator on the FastMCP instance named 'mcp' (line 31).
    @mcp.tool()
  • main.py:47-53 (helper)
    Helper function _get() used by get_run_status to make the authenticated GET request to the BitScale API.
    def _get(path: str, params: dict | None = None, timeout: int = 30) -> dict:
        """Perform an authenticated GET request against the BitScale API."""
        url = f"{BITSCALE_API_BASE}{path}"
        with httpx.Client(timeout=timeout) as client:
            response = client.get(url, headers=_headers(), params=params)
        response.raise_for_status()
        return response.json()
Behavior5/5

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

Despite no annotations, the description fully discloses behavior: polling is required, mentions rate limit implications, and details the return structure including possible status values and output format when completed. This covers all key behavioral traits.

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 (8 lines), with a clear argument section and return description. Every sentence adds value: purpose, usage, polling guidance, and output format. No wasted words.

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

Completeness5/5

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

The description is fully complete for a single-parameter polling tool. It covers the entire workflow: when to use, how to poll, what to expect in return, and how to interpret status. Output schema is implicitly described.

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?

The only parameter request_id is described as 'The request_id UUID returned by run_grid', adding crucial context beyond the schema's minimal title. With 0% schema description coverage, this fully compensates.

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 uses a specific verb 'Check' and resource 'status of a previously triggered Grid run', clearly distinguishing it from sibling tools like run_grid (which triggers) and get_grid_details (which gets grid details).

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

Usage Guidelines5/5

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

Explicitly states when to use this tool ('after run_grid returns a request_id'), provides polling advice (every 2-5 seconds), warns about rate limit, and defines termination conditions ('completed' or 'failed'). No alternatives need exclusion.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/featherflow/bitscale-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server