BigQuery MCP Server

sample_rows

Preview sample rows from any BigQuery table without writing SQL. Understand your data at a glance with a configurable row limit.

Instructions

Preview sample rows from a table without writing SQL. Useful for quickly understanding what data looks like. Limited to 1GB bytes billed.

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": "sample_rows"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Number of rows to return (default 10, max 100)
`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

A4/5.0

Behavior3/5

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

With no annotations, the description must carry the behavioral disclosure burden. It discloses a cost limit ('Limited to 1GB bytes billed') and implies read-only behavior via 'preview', but does not describe return format, sampling randomness, or pagination. This is adequate but not rich.

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 three concise sentences, with the primary purpose front-loaded, followed by a use case and a cost caveat. Every sentence adds value, and there is no redundancy or fluff.

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 the tool's simplicity, a clear output schema, and full parameter descriptions in the schema, the description covers the essential aspects: what it does, when to use it, and a key limitation. It lacks an explicit mention of alternative tools, but the context is largely 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?

Schema description coverage is 100%, so the baseline is 3. The description adds no parameter-specific detail beyond what the schema already provides, but it does not need to compensate given the high schema coverage.

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 uses a specific verb ('preview') and resource ('sample rows from a table'), clearly stating the tool's function. The phrase 'without writing SQL' distinguishes it from the sibling query tool, making the purpose 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?

The description provides clear context for when to use the tool ('quickly understanding what data looks like') and implies an alternative to writing SQL, but it does not explicitly name alternatives or state when not to use it. Thus it meets the 'clear context, no exclusions' level.

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