BigQuery MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| BQ_PROJECT | No | BigQuery project ID. Can also be provided via command line argument --project. | None |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| queryC | Executes the provided BigQuery sql statement and returns the results |
| list_tablesA | List the tables available |
| get_schemaC | Get the schema for a given table |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| bigquery://tables |
TDQS
Scored across 3 tools
Each tool has a distinct and clear purpose: query executes SQL, list_tables enumerates tables, and get_schema describes table structure. There is little risk of an agent selecting the wrong tool for a task.
list_tables and get_schema follow a consistent verb_noun snake_case pattern. The lone tool 'query' is a simple verb without a noun object, which is a minor deviation but still clear and natural for the operation.
Three tools is on the minimal side but reasonable for a focused BigQuery read/query server. Each tool serves a distinct need, though the server could support a slightly broader set without feeling bloated.
The query tool can execute arbitrary SQL, including DDL/DML, so most BigQuery operations are reachable indirectly. The main gaps are convenience features like listing datasets or managing query jobs, but agents can work around these with SQL and INFORMATION_SCHEMA queries.