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poddubnyoleg

Lightdash MCP Server

by poddubnyoleg
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Python
Remote

get-explore-schema

Retrieve the complete schema of a table including dimensions, metrics, joins, and field details. Use this before creating charts to find correct field IDs and understand data structure.

Instructions

Get the complete schema for an explore/table including all available dimensions, metrics, and joins.

This is essential before creating charts to understand what fields exist and their types.

Returns:

  • Base table information: Name, label, description

  • All dimensions by table: Field IDs, types, labels, descriptions

  • All metrics by table: Field IDs, types, SQL, labels, descriptions

  • Joins: How tables are connected, join types, join conditions

  • Summary statistics: Counts of tables, dimensions, metrics

Field information includes:

  • fieldId: Use this exact value in chart queries (format: table_fieldname)

  • type: Field data type (string, number, date, timestamp, etc.)

  • label: Human-readable name

  • description: What the field represents

  • hidden: Whether field is hidden by default

  • sql: For metrics, the SQL expression used

When to use:

  • Before creating any chart - to find correct field IDs

  • To understand available data and metrics

  • To discover join relationships between tables

  • To find field types for proper formatting

  • To explore what analysis is possible with a data model

Best practices:

  1. Start with get-catalog or get-metrics-catalog to find relevant explores

  2. Use get-explore-schema on specific explores to get detailed field information

  3. Copy exact fieldId values when building chart queries

  4. Check field descriptions to ensure you're using the right data

Hidden fields: By default, hidden fields are excluded. Set include_hidden=true to see all fields including internal/technical ones.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_nameYesName of the table/explore to introspect. This is the exploreName from your dbt models (e.g., 'snowplow__events_processed', 'wallet_users', 'orders'). Use get-catalog to discover available explore names.
include_hiddenNoOptional: Include hidden fields in the response (default: false). Hidden fields are typically internal or technical fields not meant for general use.

Tool Definition Quality

A4.8/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses return structure (base table info, dimensions, metrics, joins, summary stats), field details, and hidden fields behavior. It does not mention idempotency or side effects, but for a read-only tool this is sufficient.

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?

The description is well-structured with sections, bullet points, and clear formatting. It is detailed but not verbose; every sentence adds value. Information is front-loaded with the core purpose, then detailed sections.

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?

Despite having no output schema, the description provides extensive detail on return content (field IDs, types, labels, SQL, descriptions, joins). For a schema retrieval tool, this covers all necessary aspects and compensates fully for the lack of output schema.

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 description coverage is 100% (both parameters documented). The description adds value beyond schema: for table_name it gives concrete examples and references get-catalog; for include_hidden it clarifies what hidden fields are. This helps the agent use parameters correctly.

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 'Get the complete schema for an explore/table including all available dimensions, metrics, and joins.' It uses a specific verb and resource, and distinguishes from sibling tools like get-catalog (which lists explores) and run-chart-query (which uses the schema).

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 includes a 'When to use' section listing explicit scenarios (e.g., before creating any chart) and 'Best practices' with sequential steps referencing sibling tools like get-catalog. It provides clear context and exclusions.

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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