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BigQuery MCP

by deBilla
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Python
Hybrid

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    Related Servers

    • BigQuery MCP server

      LucasHild
      A
      license
      Not graded
      quality
      D
      maintenance
      A Model Context Protocol server that provides access to BigQuery. This server enables LLMs to inspect database schemas and execute queries.
      130
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    • BigQuery MCP server

      fastmcp-me
      A
      license
      Not graded
      quality
      D
      maintenance
      A Model Context Protocol server that provides access to BigQuery. This server enables LLMs to inspect database schemas and execute queries.
      MIT

    • bq-readonly-mcp

      mariadb-RupeshBiswas
      A
      license
      Not graded
      quality
      B
      maintenance
      A read-only BigQuery MCP server with auto-LIMIT injection, dry-run cost guard, and ADC authentication. Allows safe SQL querying of BigQuery by LLMs without risk of data modification or unexpected costs.
      26 PyPI
      1
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    • BigQuery MCP Server

      codearranger
      A
      license
      Not graded
      quality
      D
      maintenance
      Enables LLMs to interact with Google BigQuery by inspecting database schemas, listing tables, and executing SQL queries. This server facilitates seamless data analysis and management through natural language via the Model Context Protocol.
      MIT
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4.4/5.0

    Scored across 14 tools

    Disambiguation5/5

    Each tool targets a distinct resource and action: discovery (list_environments/datasets/tables), schema/freshness, code assets, scheduled SQL queries, and scheduled notebooks. The list/get pairs and the freshness-vs-scheduled-query distinction are explicitly spelled out, leaving no real overlap.

    Naming Consistency5/5

    Consistent snake_case verb_noun pattern throughout (list_datasets, get_table_schema, check_table_freshness, run_query). find_code_assets_using_table is longer but follows the same convention.

    Tool Count5/5

    14 tools sit in the ideal band and each earns its place, covering distinct phases of a BigQuery workflow (discovery, schema inspection, cost-safe querying, schedule diagnosis) without redundancy.

    Completeness4/5

    The read-and-query surface is thorough: environments, datasets, tables, schema, freshness, scheduled queries, notebook schedules/runs, code assets, and ad-hoc query execution. Write/DDL operations are absent, which appears intentional for a read-only query server, but leaves a minor gap if mutation were ever expected.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources