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BigQuery MCP by InsightfulPipe

by com.insightfulpipe
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BigQuery Data

query_data
Read-only

Query BigQuery data (Step 3 (FINAL) of workflow).

REQUIRED: Complete these steps first using query_contexts:

  1. request='accounts' - Get workspace_id, brand_id, gcp_project_id AND available_actions

  2. request='actions_details' with actions=['xxxxxx', 'xxxxxx', 'xxxxxx'] - Get detailed body structure for specific action(s) using actions from available_actions

IMPORTANT: Always call step 2 to see the exact body structure before querying.

BigQuery is read-only here (OAuth scope bigquery.readonly): only SELECT queries succeed; DDL/DML/scripts are rejected by Google.

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": {
      "body": "<body>"
    },
    "name": "query_data"
  }
}

Input Schema

NameRequiredDescriptionDefault
bodyYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, but the description adds real enforcement detail: the OAuth scope is bigquery.readonly, only SELECT succeeds, and DDL/DML/scripts are rejected by Google. It does not cover result-size limits, pagination, or job/cost behavior, which keeps it short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose, then numbered steps, then the read-only constraint — every sentence carries information. The literal 'xxxxxx' placeholders in the actions example are sloppy and could momentarily confuse an agent reading them as real values.

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 an output schema exists, return values need no explanation, and the prerequisite chain plus read-only enforcement are fully covered. The only gap is the body payload shape, which is delegated to a sibling tool rather than described here.

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?

There is a single free-form 'body' object with additionalProperties:true and 0% schema description coverage, so the schema gives the agent nothing. The description compensates only indirectly by pointing to the required query_contexts step that yields the exact body structure, but it never describes the body's expected fields inline.

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?

States a specific verb and resource ('Query BigQuery data') and immediately situates it as the final step of a workflow, which distinguishes it from query_contexts (metadata discovery) and execute_action. An agent can tell what this tool does and where it sits without opening 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?

Explicitly names the two prerequisite calls on the sibling query_contexts tool, including the exact request values ('accounts', 'actions_details'), and states 'Always call step 2' before querying. The when-to-use condition and the alternative (metadata tool) are both spelled out with no inference required.

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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