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list_grids

Discover spreadsheet-like data tables in your BitScale workspace with their column definitions to identify available grids before running enrichment or formula operations.

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:87-120 (handler)
    The list_grids function retrieves a paginated list of grids from the /grids endpoint and returns them as a JSON string.
    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)
Behavior4/5

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

With no annotations, description carries full burden and succeeds: discloses pagination behavior (1-based, max 100), reveals return filtering ('only runnable columns'), explains output structure deeply, and clarifies critical data model distinction between column UUIDs vs input labels for run_grid. Missing explicit read-only safety statement.

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?

Structured with clear Args/Returns/Note sections. Despite length, every element is essential: parameter docs (schema is empty), output explanation (complex nested structure), and the Note (prevents API usage errors). No wasted prose.

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 0% input schema coverage and complex nested output, description achieves completeness by documenting all parameters, explaining paginated response structure including field meanings, and mapping relationships to sibling tools (run_grid input_labels vs column UUIDs).

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?

Excellently compensates for 0% schema coverage: documents all 3 parameters with semantics (search is case-insensitive substring), constraints (limit max 100), defaults (page: 1), and concrete examples ('leads').

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?

Opens with specific verb+resource ('List all Grids') and immediately distinguishes sibling relationships by stating to 'use this to discover available grids before running them' (contrasting with run_grid) and noting results feed into get_grid_details/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?

Provides clear temporal context ('discover...before running') and explicit workflow guidance ('Use the grid id from the results to call get_grid_details or run_grid'). Lacks explicit 'when not to use' exclusions, preventing a 5.

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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