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list_grids

List all grids in the workspace with their column definitions to discover available grids before running them. Filter by name and paginate results.

Instructions

List all Grids in the workspace with their column definitions.

Grids are spreadsheet-like tables in BitScale that hold data rows and enrichment/formula columns. Use this to discover available grids before running them.

Args: search: Optional keyword to filter grids by name (case-insensitive substring match). Example: "leads" to find lead-related grids. page: Page number for pagination (1-based, default: 1). limit: Results per page (default: 20, max: 100).

Returns: paginated list of grids, each with id, name, description, row_count, column_count, created_at, updated_at, and columns array. The columns array contains only runnable columns (type: enrichment, formula, or merge) with their id (column UUID), name, type, and dependencies.

Use the grid id from the results to call get_grid_details or run_grid. Note: the column UUIDs here are used for output_columns in run_grid. The input labels for run_grid are separate human-readable keys derived from the grid's API data source configuration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchNo
pageNo
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • main.py:86-120 (handler)
    The handler function for the 'list_grids' tool. It calls the BitScale API GET /grids endpoint with optional search, page, and limit parameters, then returns JSON-formatted results.
    @mcp.tool()
    def list_grids(
        search: str = "",
        page: int = 1,
        limit: int = 20,
    ) -> str:
        """
        List all Grids in the workspace with their column definitions.
    
        Grids are spreadsheet-like tables in BitScale that hold data rows and
        enrichment/formula columns. Use this to discover available grids before
        running them.
    
        Args:
            search: Optional keyword to filter grids by name (case-insensitive
                    substring match). Example: "leads" to find lead-related grids.
            page:   Page number for pagination (1-based, default: 1).
            limit:  Results per page (default: 20, max: 100).
    
        Returns: paginated list of grids, each with id, name, description,
        row_count, column_count, created_at, updated_at, and columns array.
        The columns array contains only runnable columns (type: enrichment,
        formula, or merge) with their id (column UUID), name, type, and
        dependencies.
    
        Use the grid id from the results to call get_grid_details or run_grid.
        Note: the column UUIDs here are used for output_columns in run_grid.
        The input labels for run_grid are separate human-readable keys derived
        from the grid's API data source configuration.
        """
        params: dict = {"page": page, "limit": limit}
        if search:
            params["search"] = search
        data = _get("/grids", params=params)
        return json.dumps(data, indent=2)
  • main.py:86-86 (registration)
    Registration of the list_grids function as an MCP tool using the @mcp.tool() decorator from FastMCP.
    @mcp.tool()
  • main.py:87-91 (schema)
    Input schema/type definitions for list_grids: search (str), page (int, default 1), limit (int, default 20), returns str.
    def list_grids(
        search: str = "",
        page: int = 1,
        limit: int = 20,
    ) -> str:
  • main.py:47-53 (helper)
    Helper function _get() used by list_grids to perform 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()
  • main.py:34-44 (helper)
    Helper function _headers() that provides auth headers used by _get() which list_grids calls.
    def _headers() -> dict:
        """Return the auth headers required by the BitScale API."""
        if not API_KEY:
            raise RuntimeError(
                "BITSCALE_API_KEY environment variable is not set. "
                "Set it before starting the server."
            )
        return {
            "X-API-KEY": API_KEY,
            "Content-Type": "application/json",
        }
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that the tool returns a paginated list with column details, explains the column UUID usage for run_grid, and mentions case-insensitive substring matching for search. No behavioral contradictions.

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 Args and Returns sections, but could be slightly more concise. However, it is efficient and front-loaded with key information.

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 tool has 3 parameters and an output schema, the description is thorough. It covers return format, pagination, column details, and usage hints for related tools. No gaps.

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?

Schema coverage is 0%, so description fully compensates. It clearly explains each parameter: search (optional, case-insensitive substring match), page (1-based, default 1), limit (default 20, max 100), with examples.

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 tool lists all grids with column definitions, defines 'grids' as spreadsheet-like tables, and distinguishes from siblings by noting it is used to discover grids before calling get_grid_details or run_grid.

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 explains when to use (discover available grids before running them) and how to use the results (grid id for other tools), but does not explicitly state when not to use or mention alternatives beyond the sibling tools.

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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