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poddubnyoleg

Lightdash MCP Server

by poddubnyoleg

run-dashboard-tiles

Fetch dashboard configuration and execute selected tiles concurrently to download chart data as CSV. Supports running single, multiple, or all tiles for efficient data export.

Instructions

Run one or multiple dashboard tiles (or all tiles) concurrently.

This tool fetches the dashboard configuration once and then executes the selected tiles in parallel.

When to use:

  • To download the entire dashboard data.

  • To get data from multiple specific tiles at once (or from single tile).

Returns:

  • A dictionary where keys are tile UUIDs and values contain:

    • title: Tile title

    • status: "success" or "error"

    • csv_data: CSV-formatted string with headers, data rows, and metadata (for successful tiles)

    • error: Error message (for failed tiles)

  • Each CSV data includes a metadata comment line with row count and field information

  • If a tile fails to execute, the value will contain an error message.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dashboard_nameYesName of the dashboard (supports partial matching)
tile_uuidsNoOptional: List of tile UUIDs to execute. If omitted or empty, ALL chart tiles on the dashboard will be executed.
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses that the tool fetches dashboard configuration once and executes tiles in parallel. It also describes the return format including error handling for failed tiles. This provides sufficient behavioral context.

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 reasonably concise with clear sections for 'When to use' and 'Returns'. It is front-loaded with the main action. There is no redundant information, and each sentence adds value.

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

Completeness4/5

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

Given no output schema, the description adequately describes the return values: a dictionary with keys for tile UUIDs, containing title, status, csv_data (with metadata), and error. It covers input parameters and behavior (parallel execution, fetching config once). It is complete for an AI agent to understand the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds valuable context: explaining that if tile_uuids is omitted or empty, all chart tiles are executed. It also details the return structure (tile UUIDs as keys, status, csv_data, error). This goes beyond what the schema provides.

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 it runs one or multiple dashboard tiles concurrently. It distinguishes from sibling tools like run-chart-query by focusing on dashboard tiles. The verb 'run' and resource 'dashboard tiles' are specific and unambiguous.

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 includes a 'When to use' section that lists appropriate scenarios: downloading entire dashboard data or getting data from multiple specific tiles. It does not explicitly state when not to use, but the context is clear enough for an AI to decide.

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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