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BigQuery MCP

BigQuery MCP

by deBilla
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

list_datasets

Read-onlyIdempotent

Discover which BigQuery datasets exist in your project before querying. Runs without scanning data; optionally choose a configured environment.

Instructions

List the BigQuery datasets available in the data platform project.

Call this first to discover what data exists. Free — scans no data.

Args: environment: Which configured BigQuery environment to use. Omit to use the default. Call list_environments to see what exists.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_datasets"
  }
}

Input Schema

NameRequiredDescriptionDefault
environmentNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent and non-destructive, so the safety profile is covered. The description adds genuinely new behavior: the call is free and scans no data, which is material cost information an agent can use when planning.

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?

Three short sentences plus a one-parameter Args block; the discovery/cost rationale is front-loaded and nothing is redundant.

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 zero-required-param, read-only listing tool with no output schema, the description covers purpose, cost, and the one parameter well. It could briefly say what a returned dataset entry looks like, which is the only remaining gap.

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 0%, so the description must carry the parameter burden, and it does: it explains that environment selects a configured BigQuery environment, that omitting it uses the default, and points to list_environments for valid values. It doesn't state accepted format/name conventions, so not a 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('List the BigQuery datasets') scoped to the data platform project, which cleanly separates it from sibling list_tables/list_code_assets. It does not explicitly contrast with list_tables, so it falls just short of a 5.

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?

Gives clear usage context ('Call this first to discover what data exists') and routes the agent to list_environments for discovering valid environment values. No explicit when-not-to-use guidance, but the intended entry-point role is unambiguous.

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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