BigQuery MCP Server

query_cost_estimate

Estimate BigQuery query cost by dry-running SQL to see bytes scanned before execution, enabling cost-aware decisions.

Instructions

Dry-run a SQL query to see how many bytes it would scan without actually executing it. Use this before running expensive queries to check cost.

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": {
      "sql": "<sql>"
    },
    "name": "query_cost_estimate"
  }
}

Input Schema

Name	Required	Description	Default
`sql`	Yes	The SQL query to estimate cost for
`project_id`	No	Override the default project ID

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.1.0Aug 2, 2026

TDQS

A4.3/5.0

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 the key behavior: dry-run, no execution, returns byte count. This is sufficient for understanding side effects, though it could mention permissions or rate limits.

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?

Two sentences, no redundancy, front-loaded with the core action. Efficiently communicates purpose and usage.

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?

For a simple tool with full schema coverage and no output schema, the description covers purpose, usage, and behavior comprehensively. The dry-run aspect makes context clear without needing more.

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 covers both parameters with descriptions, so description adds little beyond. The description of project_id as 'Override the default project ID' is already in schema; tool description adds no further semantics.

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?

Clearly states it performs a dry-run of a SQL query to estimate bytes scanned, distinguishing it from query execution. The phrase 'without actually executing it' makes the resource and behavior 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?

Explicitly advises using this before expensive queries as a cost check. It implies the alternative is actual execution, but does not name sibling tools or state when not to use it in explicit terms.

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