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get_grid_details

Retrieve full metadata for a grid, including column definitions, settings, and data sources. Use this to inspect a grid's schema before running it.

Instructions

Get full metadata for a specific Grid, including all column definitions, grid settings, and attached data sources.

Use this to inspect a grid's schema before running it — especially to understand the grid's input labels and output column UUIDs needed for the run_grid tool.

Args: grid_id: UUID of the grid. Found in the grid URL at app.bitscale.ai/grid/{gridId}, or from list_grids results.

Returns: grid id, name, description, row_count, created_at, updated_at, settings (auto_run, auto_dedupe, visibility, dedupe_column_id), columns (all columns including text, enrichment, formula, merge types with their id/key and name), and sources (data sources with schedule info).

NOTE on columns vs run_grid inputs:

  • The column 'id' values here are UUIDs — use these for the 'output_columns' parameter of run_grid to filter which outputs you want.

  • The 'inputs' parameter of run_grid uses human-readable LABELS (e.g. "company_name", "website"), NOT column UUIDs. These labels are derived from the API data source columns configured on the grid. You can find the exact input labels in the BitScale app under the grid's Data Source → BitScale API panel, or by inspecting the source column names.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
grid_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • main.py:124-156 (handler)
    The get_grid_details tool handler function. It takes a grid_id (UUID string), validates it's not empty, performs a GET request to /grids/{grid_id}, and returns the full grid metadata as formatted JSON. This includes column definitions, grid settings, and attached data sources.
    def get_grid_details(grid_id: str) -> str:
        """
        Get full metadata for a specific Grid, including all column definitions,
        grid settings, and attached data sources.
    
        Use this to inspect a grid's schema before running it — especially to
        understand the grid's input labels and output column UUIDs needed for
        the run_grid tool.
    
        Args:
            grid_id: UUID of the grid. Found in the grid URL at
                     app.bitscale.ai/grid/{gridId}, or from list_grids results.
    
        Returns: grid id, name, description, row_count, created_at, updated_at,
        settings (auto_run, auto_dedupe, visibility, dedupe_column_id),
        columns (all columns including text, enrichment, formula, merge types
        with their id/key and name), and sources (data sources with schedule info).
    
        NOTE on columns vs run_grid inputs:
        - The column 'id' values here are UUIDs — use these for the
          'output_columns' parameter of run_grid to filter which outputs
          you want.
        - The 'inputs' parameter of run_grid uses human-readable LABELS
          (e.g. "company_name", "website"), NOT column UUIDs. These labels
          are derived from the API data source columns configured on the
          grid. You can find the exact input labels in the BitScale app
          under the grid's Data Source → BitScale API panel, or by
          inspecting the source column names.
        """
        if not grid_id:
            raise ValueError("grid_id must not be empty")
        data = _get(f"/grids/{grid_id}")
        return json.dumps(data, indent=2)
  • main.py:123-124 (registration)
    The tool is registered via the @mcp.tool() decorator on line 123, which registers it with the FastMCP server instance as a callable tool named 'get_grid_details'.
    @mcp.tool()
    def get_grid_details(grid_id: str) -> str:
  • The schema/type for the input is defined in the function signature: grid_id is a required string (str type annotation). The return type is str (JSON-formatted string).
    def get_grid_details(grid_id: str) -> str:
  • main.py:47-53 (helper)
    The _get helper function performs authenticated GET requests using the BitScale API base URL, auth headers, and optional query params/timeout. It's called by get_grid_details to fetch the grid data.
    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?

With no annotations, the description carries full burden. It reveals that the operation is read-only (get metadata) and discloses important behavioral nuance: the distinction between column UUIDs (for 'output_columns') and human-readable labels (for 'inputs') in 'run_grid'. This prevents misuse.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear header, usage note, args, returns, and a critical note section. However, the 'Args' section redundantly repeats the parameter definition already in the schema. Minor verbosity prevents a perfect score.

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?

Given the complexity of grid metadata and its relationship to 'run_grid', the description covers all essential aspects: purpose, usage context, parameter details, return structure, and crucial caveat about UUIDs vs labels. The presence of an output schema makes the returns section sufficient.

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 'grid_id' has no schema description (0% coverage). The description fully compensates by explaining its format (UUID), where to find it (URL or list_grids), and its purpose, adding high value 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 clearly states the verb 'Get' and resource 'full metadata for a specific Grid'. It distinguishes itself from siblings like 'list_grids' and 'run_grid' by focusing on schema inspection. The explicit list of included metadata (column definitions, settings, sources) leaves no ambiguity.

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?

The description explicitly says 'Use this to inspect a grid's schema before running it' and explains why – to understand input labels and output column UUIDs for 'run_grid'. It also tells where to find the 'grid_id' parameter, providing clear when-to-use 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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