BigQuery & Tavily FastAPI MCP

A lightweight, secure API & MCP for accessing and querying Google BigQuery datasets and Tavily search

FastAPI

Features

• Read-only access to BigQuery datasets and tables
• Security features including query validation and dataset access control
• Full support for standard BigQuery queries with cost control
• Tavily search and web content extraction capabilities
• RESTful API with comprehensive documentation

Setup

Prerequisites

• Python 3.11 or higher
• Google Cloud Project with BigQuery enabled
• Service account with BigQuery access
• Tavily API key for search functionality

Installation

1. Clone the repository

git clone https://github.com/osushinekotan/bigquery-fastapi-mcp
cd bigquery-fastapi-mcp

2. Install dependencies

uv sync

3. Create a .env file with your configuration

BQ_PROJECT_ID=your-gcp-project-id
BQ_ALLOWED_DATASETS=dataset1,dataset2,dataset3
BQ_MAX_BYTES_BILLED=1073741824  # 1GB default
TAVILY_API_KEY=your-tavily-api-key
APP_HOST=127.0.0.1
APP_PORT=8000

4. Set up GCP authentication

# Either set the environment variable
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/your/service-account-key.json

# Or authenticate using gcloud
gcloud auth application-default login

Running the Application

uv run uvicorn app.main:app --reload

or

uv run python -m app.main

The API will be available at http://localhost:8000

API documentation will be available at http://localhost:8000/docs

API Endpoints

Health Check

• GET /health/health - Verify the API is running

BigQuery Datasets

• GET /bigquery/list_datasets - List all datasets in the project (filtered by allowed datasets)
• GET /bigquery/allowed_datasets - Get configured allowed datasets

BigQuery Tables

• GET /bigquery/tables - List all tables in allowed datasets
• GET /bigquery/tables?dataset_id=your_dataset - List tables in a specific dataset
• GET /bigquery/tables/{dataset_id}/{table_id} - Get detailed information about a specific table

BigQuery Query

• POST /bigquery/query - Execute a BigQuery query

Example request body:

{
  "query": "SELECT * FROM `project.dataset.table` LIMIT 10",
  "dry_run": true
}

Tavily Search

• POST /search/search - Search the web using Tavily

Example request body:

{
  "query": "latest developments in AI",
  "max_results": 5
}

Tavily Extract

• POST /search/extract - Extract content from web URLs

Example request body:

{
  "urls": ["https://example.com/article1", "https://example.com/article2"]
}

Security Features

• Read-only query validation (only SELECT statements are allowed)
• Dataset access control through environment configuration
• Maximum billable bytes limit with configurable thresholds

MCP server

https://github.com/tadata-org/fastapi_mcp

Connecting to the MCP Server using SSE

Once your FastAPI app with MCP integration is running, you can connect to it with any MCP client supporting SSE, such as Cursor:

1. Run your application.

2. In Cursor -> Settings -> MCP, use the URL of your MCP server endpoint (e.g., http://localhost:8000/mcp) as sse.

3. Cursor will discover all available tools and resources automatically.

Connecting to the MCP Server using mcp-proxy stdio

If your MCP client does not support SSE, for example Claude Desktop:

1. Run your application.

2. Install mcp-proxy, for example: uv tool install mcp-proxy.

3. Add in Claude Desktop MCP config file (claude_desktop_config.json):

On Windows:

{
  "mcpServers": {
    "my-api-mcp-proxy": {
      "command": "mcp-proxy",
      "args": ["http://127.0.0.1:8000/mcp"]
    }
  }
}

On MacOS:

Find the path to mcp-proxy by running in Terminal: which mcp-proxy.

{
  "mcpServers": {
    "my-api-mcp-proxy": {
      "command": "/Full/Path/To/Your/Executable/mcp-proxy",
      "args": ["http://127.0.0.1:8000/mcp"]
    }
  }
}

Find the path to mcp-proxy by running in Terminal: which uvx.

{
  "mcpServers": {
    "my-api-mcp-proxy": {
      "command": "/Full/Path/To/Your/uvx",
      "args": ["mcp-proxy", "http://127.0.0.1:8000/mcp"]
    }
  }
}

4. Claude Desktop will discover all available tools and resources automatically