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poddubnyoleg

Lightdash MCP Server

by poddubnyoleg

run-chart-query

Execute a chart query against your data warehouse and retrieve results as CSV. Use it to export data, verify chart outputs, or feed data into other tools.

Instructions

Execute a chart query and return the data results in CSV format.

Runs the chart's configured query against the data warehouse and returns the results as CSV.

Returns:

  • CSV-formatted string with headers and data rows

  • Metadata comment line with row count (format: # Metadata: {"row_count":N})

When to use:

  • To get actual data from a chart for analysis

  • To verify a chart is returning expected results

  • To export chart data programmatically

  • To preview data before creating a dashboard tile

Performance notes:

  • Large result sets may take time to execute

  • Use the limit parameter to restrict rows returned

  • Query execution happens in real-time against your warehouse

Optional limit parameter: Restricts the number of rows returned (useful for large datasets)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chart_uuidYesUUID of the chart to execute
limitNoOptional: Limit number of rows returned. Useful for large datasets. Example: 100 will return max 100 rows
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 return format (CSV with metadata), real-time execution, performance notes, and optional limit. Does not explicitly state read-only, but implied by 'execute a chart query'.

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?

Description is well-organized with sections, front-loaded with core purpose. Every sentence provides value, no fluff. Approximately 150 words.

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 2 parameters, no output schema, and moderate complexity, description covers purpose, usage, return format, and performance. Missing example or error handling, but adequate.

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

Parameters3/5

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

Schema coverage is 100%, so description adds marginal value. It reiterates limit's purpose and provides example, but chart_uuid description repeats schema. Baseline 3 appropriate.

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 'executes a chart query and returns data in CSV format.' It uses specific verb+resource, and distinguishes from siblings like run-dashboard-tiles and run-raw-query.

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 'When to use' section lists four appropriate scenarios (getting data, verifying chart, exporting, previewing). It lacks explicit exclusions or alternatives, but the usage context is clear.

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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