Db Metadata Extractor MCP

mcp-name: io.github.Optisol-Business/db-metadata-extractor-mcp

Database Metadata Extractor MCP Server

A Model Context Protocol (MCP) server that extracts and queries database schema metadata from PostgreSQL, Snowflake, SQL Server, BigQuery, and Oracle databases.

Features

• ✅ Multi-database support: PostgreSQL, Snowflake, SQL Server (MSSQL), BigQuery, Oracle
• ✅ Complete schema extraction: Tables, columns, primary keys, indexes, constraints
• ✅ Local JSON output: Saves metadata directly to local folder (no cloud required)
• ✅ Query interface: Search and filter metadata by table/column names
• ✅ Pagination support: Browse large schemas efficiently
• ✅ VS Code integration: Works with VS Code Agent Mode
• ✅ CLI customizable: Transport options (stdio, HTTP)

Installation

From PyPI

pip install db-metadata-extractor-mcp

From Source

git clone https://github.com/Optisol-Business/db-metadata-extractor-mcp.git

cd db-metadata-extractor-mcp pip install -e .

Quick Start

1. Start the MCP Server

db-metadata-extractor-mcp

The server starts in stdio mode by default and listens for MCP client connections.

2. Configure in Claude Desktop

Add to ~/.config/Claude/claude_desktop_config.json (macOS/Linux) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{

"mcpServers": { "db-metadata-extractor": { "command": "db-metadata-extractor-mcp", "args": [], "env": {} } } }

Restart Claude Desktop.

3. Use in Claude

Tell Claude:

Extract metadata from my PostgreSQL database and save it to /tmp/output

Claude will use the server's tools to extract and query your database schema.

Tools

extract_metadata

Extracts complete schema metadata from a database.

Parameters:

• db_type (required): postgresql, snowflake, sqlserver, bigquery, oracle
• output_path (required): Local directory for JSON output
• database_name: Database/schema name
• host: Database host (not needed for BigQuery/Snowflake)
• port: Database port
• username: Database user
• password: Database password
• schema_name: Specific schema (optional)
• tables: Array of table names to extract (optional)
• account: Snowflake account ID
• warehouse: Snowflake warehouse
• role_name: Snowflake role
• project_id: BigQuery project ID
• service_account_key: BigQuery service account JSON (base64 encoded)

Returns:

• File path where metadata was saved
• Summary statistics (table count, column count, etc.)

query_metadata

Query previously extracted metadata.

Parameters:

• filepath (required): Path to metadata JSON file
• table_name: Filter by table name (substring match)
• field_name: Filter by column name (substring match)
• page: Page number (default: 1)
• page_size: Results per page (default: 20)

Returns:

• Paginated table results matching filters

Examples

PostgreSQL

# Via Claude

"Extract all tables from my dev PostgreSQL database at localhost:5432"

Parameters Claude will use:

{

"db_type": "postgresql", "host": "localhost", "port": 5432, "database_name": "dev_db", "username": "postgres", "password": "your_password", "output_path": "/tmp/db_metadata" }

Snowflake

"Extract schema from Snowflake account XYZ123"

Parameters:

{

"db_type": "snowflake", "account": "XYZ123", "username": "your_user", "password": "your_password", "warehouse": "COMPUTE_WH", "role_name": "ANALYST", "database_name": "PRODUCTION", "output_path": "C:/metadata" }

BigQuery

"Extract metadata from BigQuery project my-project-123"

Parameters:

{

"db_type": "bigquery", "project_id": "my-project-123", "service_account_key": "base64_encoded_json_key", "output_path": "/tmp/bq_metadata" }

Advanced Usage

Custom Transport

Start with HTTP transport:

db-metadata-extractor-mcp --transport streamable-http --port 3000

Environment Variables

# Set database credentials via env

export DB_HOST=localhost export DB_USER=postgres export DB_PASSWORD=secret

db-metadata-extractor-mcp

Output Format

The extracted metadata is saved as a JSON file with structure:

{

"source": { "db_type": "postgresql", "extracted_at": "2026-04-09T14:30:00", "host": "localhost" }, "schemas": [ { "schema_name": "public", "tables": [ { "table_name": "users", "columns": [ { "column_name": "id", "data_type": "int", "is_nullable": false, "is_primary_key": true }, { "column_name": "email", "data_type": "varchar", "is_nullable": false } ], "indexes": [ { "index_name": "users_email_idx", "columns": ["email"] } ] } ] } ] }

Requirements

• Python 3.8+
• For PostgreSQL: psycopg2-binary
• For Snowflake: snowflake-connector-python
• For SQL Server: pyodbc, pymssql
• For BigQuery: google-cloud-bigquery
• For Oracle: oracledb

Troubleshooting

Connection Errors

Problem: "Unable to connect to database"

Solution: Verify credentials and network access:

# Test PostgreSQL connection

psql -h localhost -U postgres -c "SELECT 1"

Test Snowflake

snowsql -a XYZ123 -u your_user

Permission Errors

Problem: "Access denied" or "insufficient permissions"

Solution: Ensure database user has:

• SELECT on tables
• USAGE on schemas
• CONNECT on databases

Large Schema Timeouts

Problem: Extraction times out on large databases

Solution: Extract specific schema/tables:

{

"schema_name": "public", "tables": ["users", "orders"] // Specify subset }

License

MIT License - See LICENSE file

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create feature branch
3. Submit pull request

Support

• GitHub Issues: https://github.com/Optisol-Business/db-metadata-extractor-mcp/issues
• Documentation: See MCP_REGISTRY_GUIDE.md

Links

• PyPI: https://pypi.org/project/db-metadata-extractor-mcp/
• GitHub: https://github.com/Optisol-Business/db-metadata-extractor-mcp
• MCP Spec: https://modelcontextprotocol.io/