PostgreSQL MCP Server

The PostgreSQL MCP Server bridges the gap between AI assistants and relational data by exposing a rich set of CRUD tools over the Model Context Protocol. It allows an assistant to read, write, update, and delete rows in pre‑configured PostgreSQL tables without writing raw SQL, while still giving developers full control over which columns and operations are permitted. This abstraction is especially valuable when building data‑centric conversational agents, automated reporting tools, or AI‑driven analytics pipelines that need to interact with production databases safely and predictably.

At its core, the server runs in stdio mode using the FastMCP library. Clients connect via standard MCP streams, sending requests that reference one of several built‑in tools: , , , , , , and . Each tool translates a declarative request into a parameterized SQL statement, executes it against the configured database, and returns a JSON payload that includes status information, any affected records, or error details. Because the server validates table and column names against a YAML configuration file, developers can enforce fine‑grained access controls that prevent accidental data leaks or destructive operations.

Key capabilities include:

• Table‑level and column‑level access control: Only tables and columns defined in the config are exposed, mitigating accidental exposure of sensitive data.
• Schema introspection: The tool lets assistants query column types, defaults, and constraints, enabling dynamic UI generation or data validation.
• Custom SQL execution: When higher flexibility is needed, the tool allows arbitrary queries with parameter binding, though it should be used sparingly to avoid injection risks.
• Standardized response format: Every tool returns a consistent JSON structure, simplifying error handling and result parsing in client applications.

Typical use cases span from chat‑based data entry—where a user can create or update records through natural language—to automated reporting, where an assistant pulls data, formats it, and presents insights. In a CI/CD pipeline, the server can be invoked by a bot to seed test databases or clean up after tests. Because it operates over MCP, any AI platform that understands the protocol can integrate this server with minimal friction, turning a plain PostgreSQL instance into an AI‑friendly data service.

Unique advantages of this MCP server are its declarative configuration model and tight integration with the MCP ecosystem. By separating data access rules from code, developers can audit permissions in a single YAML file, and the server’s standard toolset eliminates boilerplate SQL. Combined with FastMCP’s lightweight runtime, it offers a secure, maintainable, and extensible bridge between conversational AI and relational data stores.