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MCP BigQuery Server

by Mousten
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MCP BigQuery Server

A FastMCP server for securely accessing BigQuery datasets with intelligent caching, schema evolution tracking, and query analytics via Supabase integration.

Features

  • Multiple Transport Methods: HTTP, Stdio, and SSE (Server-Sent Events)

  • BigQuery Integration: Secure access to BigQuery datasets and tables

  • Intelligent Caching: Query result caching with TTL management and dependency tracking

  • Supabase Knowledge Base: Enhanced metadata storage and business context

  • Query Analytics: Performance analysis and optimization recommendations

  • Schema Evolution Tracking: Monitor table schema changes over time

  • AI-Powered Suggestions: Query recommendations based on usage patterns

  • Real-time Events: Server-Sent Events for query monitoring and system status

  • Read-only Queries: Safety-first approach with read-only SQL execution

  • Row Level Security: User-based access control and cache isolation

  • Comprehensive API: RESTful endpoints and MCP protocol support

Installation

Using uv (recommended):

# Clone the repository git clone <repository-url> cd mcp-bigquery-server # Create virtual environment and install dependencies uv venv source .venv/bin/activate # On Windows: .venv\Scripts\activate uv pip install -e ".[dev]"

Configuration

  1. Copy the example environment file:

cp .env.example .env

  1. Edit .env with your BigQuery and Supabase details:

# BigQuery Configuration PROJECT_ID=your-project-id LOCATION=US KEY_FILE=/path/to/your/service-account-key.json # Optional DEFAULT_USER_ID=your-default-user-id # Optional # Supabase Configuration (for enhanced features) SUPABASE_URL=https://your-project.supabase.co SUPABASE_SERVICE_KEY=your-service-role-key # Recommended for full access SUPABASE_ANON_KEY=your-anon-key # Alternative with RLS

Supabase Setup

For enhanced caching and analytics features, you'll need a Supabase project with the following tables and policies.

  • query_cache - Stores cached query results

  • table_dependencies - Tracks table dependencies for cache invalidation

  • query_history - Historical query execution patterns

  • query_templates - Reusable query templates

  • column_documentation - Business context for table columns

  • event_log - System event tracking

Run these SQL queries in your Supabase SQL editor to set up the schema:

-- Enable UUID extension CREATE EXTENSION IF NOT EXISTS "uuid-ossp"; -- 1. Query Result Caching Tables CREATE TABLE query_cache ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), query_hash TEXT UNIQUE NOT NULL, sql_query TEXT NOT NULL, result_data JSONB NOT NULL, metadata JSONB NOT NULL, -- bytes processed, execution time, etc. created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), expires_at TIMESTAMP WITH TIME ZONE NOT NULL, hit_count INTEGER DEFAULT 0 ); -- Indexes for query_cache CREATE INDEX idx_query_cache_hash ON query_cache(query_hash); CREATE INDEX idx_query_cache_expires ON query_cache(expires_at); CREATE INDEX idx_query_cache_created ON query_cache(created_at); CREATE TABLE table_dependencies ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), query_cache_id UUID REFERENCES query_cache(id) ON DELETE CASCADE, project_id TEXT NOT NULL, dataset_id TEXT NOT NULL, table_id TEXT NOT NULL, created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW() ); -- Indexes for table_dependencies CREATE INDEX idx_table_deps_lookup ON table_dependencies(project_id, dataset_id, table_id); CREATE INDEX idx_table_deps_cache ON table_dependencies(query_cache_id); -- 2. Schema Evolution & Knowledge Base Tables CREATE TABLE schema_snapshots ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), project_id TEXT NOT NULL, dataset_id TEXT NOT NULL, table_id TEXT NOT NULL, schema_version INTEGER NOT NULL DEFAULT 1, schema_data JSONB NOT NULL, row_count BIGINT, size_bytes BIGINT, created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW() ); -- Indexes for schema_snapshots CREATE UNIQUE INDEX idx_schema_version ON schema_snapshots(project_id, dataset_id, table_id, schema_version); CREATE INDEX idx_schema_table ON schema_snapshots(project_id, dataset_id, table_id); CREATE TABLE column_documentation ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), project_id TEXT NOT NULL, dataset_id TEXT NOT NULL, table_id TEXT NOT NULL, column_name TEXT NOT NULL, description TEXT, business_rules TEXT[], sample_values JSONB, data_quality_notes TEXT, updated_by TEXT, updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW() ); -- Indexes for column_documentation CREATE UNIQUE INDEX idx_column_docs_unique ON column_documentation(project_id, dataset_id, table_id, column_name); CREATE INDEX idx_column_docs_table ON column_documentation(project_id, dataset_id, table_id); -- 3. Query Analytics & Pattern Recognition Tables CREATE TABLE query_history ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), user_id TEXT, sql_query TEXT NOT NULL, execution_time_ms INTEGER, bytes_processed BIGINT, success BOOLEAN NOT NULL, error_message TEXT, tables_accessed TEXT[], created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW() ); -- Indexes for query_history CREATE INDEX idx_query_history_user ON query_history(user_id); CREATE INDEX idx_query_history_success ON query_history(success); CREATE INDEX idx_query_history_created ON query_history(created_at); CREATE INDEX idx_query_history_tables ON query_history USING GIN(tables_accessed); CREATE TABLE query_templates ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), name TEXT NOT NULL, description TEXT, template_sql TEXT NOT NULL, parameters JSONB NOT NULL DEFAULT '{}', usage_count INTEGER DEFAULT 0, tags TEXT[], created_by TEXT, created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW() ); -- Indexes for query_templates CREATE INDEX idx_query_templates_usage ON query_templates(usage_count DESC); CREATE INDEX idx_query_templates_tags ON query_templates USING GIN(tags); -- 4. Real-time Event Tracking CREATE TABLE event_log ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), event_type TEXT NOT NULL, event_data JSONB NOT NULL, user_id TEXT, session_id TEXT, created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW() ); -- Indexes for event_log CREATE INDEX idx_event_log_type ON event_log(event_type); CREATE INDEX idx_event_log_user ON event_log(user_id); CREATE INDEX idx_event_log_session ON event_log(session_id); CREATE INDEX idx_event_log_created ON event_log(created_at); -- 5. User Preferences & Settings CREATE TABLE user_preferences ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), user_id TEXT UNIQUE NOT NULL, preferences JSONB NOT NULL DEFAULT '{}', query_defaults JSONB NOT NULL DEFAULT '{}', favorite_queries UUID[] DEFAULT '{}', created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW() ); -- Row Level Security (RLS) Policies for BigQuery Cache Tables -- First, ensure RLS is enabled on the tables ALTER TABLE query_cache ENABLE ROW LEVEL SECURITY; ALTER TABLE query_history ENABLE ROW LEVEL SECURITY; ALTER TABLE table_dependencies ENABLE ROW LEVEL SECURITY; ALTER TABLE schema_snapshots ENABLE ROW LEVEL SECURITY; ALTER TABLE column_documentation ENABLE ROW LEVEL SECURITY; ALTER TABLE query_templates ENABLE ROW LEVEL SECURITY; ALTER TABLE event_log ENABLE ROW LEVEL SECURITY; -- Option 1: Allow all operations (least secure, but simplest for development) -- Use this during development/testing phases -- Allow all operations on query_cache CREATE POLICY "Allow all operations on query_cache" ON query_cache FOR ALL USING (true) WITH CHECK (true); -- Allow all operations on query_history CREATE POLICY "Allow all operations on query_history" ON query_history FOR ALL USING (true) WITH CHECK (true); -- Allow all operations on table_dependencies CREATE POLICY "Allow all operations on table_dependencies" ON table_dependencies FOR ALL USING (true) WITH CHECK (true); -- Allow all operations on schema_snapshots CREATE POLICY "Allow all operations on schema_snapshots" ON schema_snapshots FOR ALL USING (true) WITH CHECK (true); -- Allow all operations on column_documentation CREATE POLICY "Allow all operations on column_documentation" ON column_documentation FOR ALL USING (true) WITH CHECK (true); -- Allow all operations on query_templates CREATE POLICY "Allow all operations on query_templates" ON query_templates FOR ALL USING (true) WITH CHECK (true); -- Allow all operations on event_log CREATE POLICY "Allow all operations on event_log" ON event_log FOR ALL USING (true) WITH CHECK (true); -- Option 2: User-based policies (more secure) -- Uncomment and use these instead if you have user authentication /* -- Allow users to manage their own cache entries CREATE POLICY "Users can manage own cache entries" ON query_cache FOR ALL USING (auth.uid()::text = user_id OR user_id IS NULL); -- Allow users to manage their own query history CREATE POLICY "Users can manage own query history" ON query_history FOR ALL USING (auth.uid()::text = user_id OR user_id IS NULL); -- Allow all operations on system tables (no user-specific data) CREATE POLICY "Allow system operations" ON table_dependencies FOR ALL USING (true) WITH CHECK (true); CREATE POLICY "Allow system operations" ON schema_snapshots FOR ALL USING (true) WITH CHECK (true); CREATE POLICY "Allow system operations" ON column_documentation FOR ALL USING (true) WITH CHECK (true); CREATE POLICY "Allow system operations" ON query_templates FOR ALL USING (true) WITH CHECK (true); -- Allow users to create their own event log entries CREATE POLICY "Users can create event logs" ON event_log FOR INSERT WITH CHECK (auth.uid()::text = user_id OR user_id IS NULL); CREATE POLICY "Users can read event logs" ON event_log FOR SELECT USING (auth.uid()::text = user_id OR user_id IS NULL); */ -- Option 3: Service role policies (for backend services) -- If your application uses a service role key, you might want to create -- policies that allow the service role to perform all operations /* -- Create a function to check if the current role is the service role CREATE OR REPLACE FUNCTION is_service_role() RETURNS BOOLEAN AS $$ BEGIN RETURN current_setting('role') = 'service_role'; EXCEPTION WHEN others THEN RETURN false; END; $$ LANGUAGE plpgsql SECURITY DEFINER; -- Service role policies CREATE POLICY "Service role can manage query_cache" ON query_cache FOR ALL USING (is_service_role()) WITH CHECK (is_service_role()); CREATE POLICY "Service role can manage query_history" ON query_history FOR ALL USING (is_service_role()) WITH CHECK (is_service_role()); */ -- Triggers for automatic timestamp updates CREATE OR REPLACE FUNCTION update_updated_at_column() RETURNS TRIGGER AS $$ BEGIN NEW.updated_at = NOW(); RETURN NEW; END; $$ LANGUAGE plpgsql; CREATE TRIGGER update_column_documentation_updated_at BEFORE UPDATE ON column_documentation FOR EACH ROW EXECUTE FUNCTION update_updated_at_column(); CREATE TRIGGER update_query_templates_updated_at BEFORE UPDATE ON query_templates FOR EACH ROW EXECUTE FUNCTION update_updated_at_column(); CREATE TRIGGER update_user_preferences_updated_at BEFORE UPDATE ON user_preferences FOR EACH ROW EXECUTE FUNCTION update_updated_at_column(); -- Cache Cleanup Function CREATE OR REPLACE FUNCTION cleanup_expired_cache() RETURNS INTEGER AS $$ DECLARE deleted_count INTEGER; BEGIN DELETE FROM query_cache WHERE expires_at < NOW(); GET DIAGNOSTICS deleted_count = ROW_COUNT; RETURN deleted_count; END; $$ LANGUAGE plpgsql;

These tables enable:

  • Query result caching and dependency tracking

  • Schema evolution and documentation

  • Query analytics and pattern recognition

  • Real-time event logging

  • User preferences and settings

Row Level Security (RLS) is enabled for all tables.
You can choose from several RLS policy options:

  • Option 1: Allow all operations (for development/testing)

  • Option 2: User-based policies (recommended for production with authentication)

  • Option 3: Service role policies (for backend services)

Customize the RLS policies as needed for your environment.

The server will work without Supabase but with limited functionality.

Usage

Command Line

# HTTP mode (default) mcp-bigquery --transport http --host 0.0.0.0 --port 8000 # Stdio mode (for MCP clients) mcp-bigquery --transport stdio # SSE mode mcp-bigquery --transport sse --host 0.0.0.0 --port 8000

Python API

from mcp_bigquery.main import main import sys # Set command line arguments sys.argv = ['mcp-bigquery', '--transport', 'http', '--port', '8000'] main()

Using with Claude Desktop

To use this MCP BigQuery server with Claude Desktop, you need to configure it in your Claude Desktop configuration file.

1. Install and Configure the Server

First, ensure the server is installed and configured:

# Clone and install the server git clone <repository-url> cd mcp-bigquery-server uv venv source .venv/bin/activate # On Windows: .venv\Scripts\activate uv pip install -e ".[dev]" # Set up environment variables cp .env.example .env # Edit .env with your BigQuery and Supabase project details

2. Configure Claude Desktop

Add the server to your Claude Desktop configuration file:

Configuration file locations:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows: %APPDATA%\Claude\claude_desktop_config.json

For macOS/Linux:

{ "mcpServers": { "mcp-bigquery": { "command": "/path/to/your/project/.venv/bin/mcp-bigquery", "args": ["--transport", "stdio"], "env": { "PROJECT_ID": "your-project-id", "LOCATION": "US", "KEY_FILE": "/path/to/your/service-account-key.json", "SUPABASE_URL": "https://your-project.supabase.co", "SUPABASE_SERVICE_KEY": "your-service-role-key", "DEFAULT_USER_ID": "your-user-id" } } } }

For Windows:

{ "mcpServers": { "mcp-bigquery": { "command": "C:\\path\\to\\your\\project\\.venv\\Scripts\\mcp-bigquery.exe", "args": ["--transport", "stdio"], "env": { "PROJECT_ID": "your-project-id", "LOCATION": "US", "KEY_FILE": "C:\\path\\to\\your\\service-account-key.json", "SUPABASE_URL": "https://your-project.supabase.co", "SUPABASE_SERVICE_KEY": "your-service-role-key", "DEFAULT_USER_ID": "your-user-id" } } } }

3. Authentication Setup

BigQuery Authentication - Choose one of two authentication methods:

Option A: Service Account Key File

  1. Create a service account in Google Cloud Console

  2. Download the JSON key file

  3. Set the KEY_FILE environment variable to the path of this file

Option B: Default Credentials

  1. Install and configure Google Cloud SDK: gcloud auth application-default login

  2. Remove the KEY_FILE from the environment variables

Supabase Authentication (Optional but recommended):

  1. Create a Supabase project

  2. Get your project URL and service role key from the Supabase dashboard

  3. Set up the required database schema (see Supabase Setup section)

4. Restart Claude Desktop

After saving the configuration file, restart Claude Desktop completely for the changes to take effect.

5. Using the Server

Once configured, you can interact with your BigQuery data through Claude Desktop with enhanced capabilities:

Basic Operations:

  • "What datasets do I have available in BigQuery?"

  • "Show me the schema for the [dataset].[table] table"

  • "Run a query to get the first 10 rows from [dataset].[table]"

Enhanced Features (with Supabase):

  • "Analyze the performance of my recent queries"

  • "What query suggestions do you have for the sales table?"

  • "Show me the schema changes for [dataset].[table] over the last month"

  • "Explain what the customer_events table is used for"

  • "What are the cache statistics?"

Streamlit AI Analyst App

The repository ships with a Streamlit front-end (streamlit_app/app.py) that wraps the MCP BigQuery server in an interactive "AI data analyst" experience. It uses an OpenAI model to translate natural language questions into safe, read-only BigQuery SQL, executes the query through the MCP server, and summarises the results in real time.

Prerequisites

  • A running instance of the MCP BigQuery server (HTTP transport).

  • Python dependencies installed (uv pip install -e .).

  • An OpenAI API key available as an environment variable (OPENAI_API_KEY) or entered in the UI.

Running the Streamlit app

# Ensure the MCP server is running locally (default assumes http://localhost:8005) export OPENAI_API_KEY="sk-..." streamlit run streamlit_app/app.py

The sidebar lets you configure the MCP base URL, user/session identifiers, query cost controls and the OpenAI model. Selecting a dataset and optional tables shares schema information with the agent to improve SQL generation. Ask questions in the chat interface and the assistant will:

  1. Propose a BigQuery query plan using the provided metadata and best practices.

  2. Execute the SQL via the MCP server, respecting caching and maximum bytes billed.

  3. Return a Markdown summary, preview table, downloadable CSV and the executed SQL for transparency.

API Endpoints

Resources

  • GET /resources/list - List all available datasets and tables

  • GET /bigquery/{project_id}/{dataset_id}/{table_id} - Get table metadata

Tools

  • POST /tools/execute_bigquery_sql - Execute read-only SQL queries with caching

  • POST /tools/get_datasets - Get list of datasets with metadata

  • POST /tools/get_tables - Get tables in a dataset with documentation

  • POST /tools/get_table_schema - Get table schema with business context

  • POST /tools/get_query_suggestions - Get AI-powered query recommendations

  • POST /tools/explain_table - Get comprehensive table documentation

  • POST /tools/analyze_query_performance - Analyze query performance patterns

  • POST /tools/get_schema_changes - Track schema evolution over time

  • POST /tools/manage_cache - Cache management operations

  • POST /tools/health_check - System health check

Events (SSE)

  • GET /events/system - System status events

  • GET /events/queries - Query execution events

  • GET /events/resources - Resource update events

Health

  • GET /health - Health check endpoint

MCP Tools and Resources

Resources

  • resources://list - List all BigQuery resources

  • bigquery://{project}/{dataset}/{table} - Access specific table metadata

Tools

Core BigQuery Tools

  • execute_bigquery_sql - Execute a read-only SQL query with intelligent caching

    • Parameters: sql, maximum_bytes_billed, use_cache, user_id, force_refresh

  • get_datasets - Get list of datasets with metadata

  • get_tables - Get tables in a dataset with column documentation

  • get_table_schema - Get comprehensive table schema details

    • Parameters: dataset_id, table_id, include_samples, include_documentation

Enhanced Analytics Tools (requires Supabase)

  • get_query_suggestions - Get AI-powered query recommendations

    • Parameters: tables_mentioned, query_context, limit, user_id

  • explain_table - Get comprehensive table documentation and business context

    • Parameters: project_id, dataset_id, table_id, include_usage_stats, user_id

  • analyze_query_performance - Analyze historical query performance patterns

    • Parameters: sql, tables_accessed, time_range_hours, user_id, include_recommendations

  • get_schema_changes - Track schema evolution and changes over time

    • Parameters: project_id, dataset_id, table_id, limit, include_impact_analysis, user_id

System Management Tools

  • manage_cache - Comprehensive cache management operations

    • Parameters: action, target, project_id, dataset_id, table_id, user_id

  • health_check - System health check including BigQuery, Supabase, and cache status

    • Parameters: user_id

Intelligent Caching System

The server includes a sophisticated caching system powered by Supabase:

Features

  • Query Result Caching: Automatic caching of query results with configurable TTL

  • Table Dependency Tracking: Cache invalidation based on table modifications

  • Cache Statistics: Hit rates, performance metrics, and usage analytics

  • User-based Isolation: Row Level Security for multi-tenant environments

  • Automatic Cleanup: Expired cache entry removal

Cache Management

# Cache a query result (automatic) result = await execute_bigquery_sql(sql="SELECT * FROM dataset.table", use_cache=True) # Force cache refresh result = await execute_bigquery_sql(sql="SELECT * FROM dataset.table", force_refresh=True) # Get cache statistics stats = await manage_cache(action="stats") # Clean up expired entries cleanup = await manage_cache(action="cleanup")

Development

Setup Development Environment

# Install with development dependencies uv pip install -e ".[dev]" # Run tests pytest # Run with coverage pytest --cov=src/mcp_bigquery --cov-report=html # Format code black src/ tests/ isort src/ tests/ # Type checking mypy src/

Project Structure

mcp-bigquery-server/ ├── src/mcp_bigquery/ # Main package │ ├── config/ # Configuration management │ ├── core/ # Core utilities (BigQuery client, Supabase client, JSON encoder) │ ├── events/ # Event management system │ ├── handlers/ # Business logic handlers │ │ ├── resources.py # Resource handlers │ │ └── tools.py # Tool handlers (query execution, analytics) │ ├── api/ # FastAPI and FastMCP applications │ ├── routes/ # FastAPI route definitions │ └── main.py # Entry point ├── tests/ # Test suite ├── pyproject.toml # Project configuration └── README.md # This file

Authentication

BigQuery Authentication

The server supports two authentication methods:

  1. Service Account Key File: Specify the path in the KEY_FILE environment variable

  2. Default Credentials: Uses Google Cloud SDK default credentials if no key file is provided

Supabase Authentication

  • Service Role Key: Full access to all tables (recommended for server deployment)

  • Anonymous Key: Limited access with Row Level Security (RLS) policies

Security

  • All SQL queries are restricted to read-only operations

  • Forbidden keywords (INSERT, UPDATE, DELETE, CREATE, DROP, ALTER) are blocked

  • Project ID validation ensures queries only run against the configured project

  • Configurable query cost limits via maximum_bytes_billed parameter

  • Row Level Security (RLS) support for multi-tenant deployments

  • User-based cache isolation and access control

Event Streaming

The server provides real-time events via Server-Sent Events (SSE):

  • System Events: Server health, connection status, Supabase connectivity

  • Query Events: Query start, progress, completion, errors, cache hits/misses

  • Resource Events: Dataset and table updates, schema changes

  • Analytics Events: Performance insights, usage patterns

Performance Considerations

  • Query Caching: Significantly reduces BigQuery costs and improves response times

  • Connection Pooling: Efficient BigQuery client management

  • Async Operations: Non-blocking I/O for better concurrency

  • Lazy Loading: Supabase connections initialized only when needed

  • Cache Optimization: Intelligent cache key generation and dependency tracking

Monitoring and Observability

The server provides comprehensive monitoring capabilities:

  • Health Checks: BigQuery and Supabase connectivity status

  • Cache Metrics: Hit rates, storage usage, performance statistics

  • Query Analytics: Execution patterns, cost analysis, optimization recommendations

  • Event Logging: Detailed audit trails for all operations

  • Error Tracking: Comprehensive error logging and reporting

Contributing

  1. Fork the repository

  2. Create a feature branch

  3. Make your changes

  4. Add tests for new functionality

  5. Run the test suite

  6. Submit a pull request

License

[Add your license information here]

Changelog

v0.2.0

  • Added Supabase integration for enhanced caching and analytics

  • Implemented intelligent query caching with table dependency tracking

  • Added AI-powered query suggestions and table explanations

  • Enhanced schema evolution tracking capabilities

  • Improved performance analysis and optimization recommendations

  • Added comprehensive event logging and audit trails

  • Implemented Row Level Security (RLS) support for multi-tenant deployments

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