BigQuery MCP Server

describe_table

Retrieve detailed schema for a BigQuery table: column names, types, descriptions, row count, size, partitioning, and clustering.

Instructions

Get detailed schema information for a specific BigQuery table including column names, types, descriptions, row count, size, partitioning, and clustering.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset": "<dataset>",
      "table": "<table>"
    },
    "name": "describe_table"
  }
}

Input Schema

Name	Required	Description
`table`	Yes	Table name
`dataset`	Yes	Dataset name
`project_id`	No	Override the default project ID

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.1.0Aug 2, 2026

TDQS

A3.8/5.0

Behavior3/5

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

With no annotations, the description carries the transparency burden. It lists return contents but does not explicitly state that the operation is read-only, side-effect-free, or describe permission requirements. The verb 'Get' implies safety, but this is not made explicit.

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 a single, front-loaded sentence that efficiently conveys the purpose and key details with no irrelevant content. It earns its place without redundancy.

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?

For a simple tool with no output schema, the description sufficiently communicates what the tool returns and its scope. It does not cover edge cases or prerequisites, but given the schema clarity and tool purpose, it is adequately complete.

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?

The input schema already covers all parameters with 100% description coverage. The tool description does not add significant meaning beyond the schema, though it reinforces that the table is a 'specific BigQuery table' and that schema details are returned.

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's function: 'Get detailed schema information for a specific BigQuery table' and enumerates specific return details (column names, types, descriptions, row count, size, partitioning, clustering). This distinguishes it from siblings like list_tables or sample_rows.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool (when schema details are needed) but does not explicitly provide when-to-use vs alternatives or exclusions. It is clear enough for basic inference but lacks explicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.