server-postgres is a Model Context Protocol (MCP) server designed to provide AI models with secure, read-only access to PostgreSQL databases. It empowers Large Language Models (LLMs) to inspect database schemas and execute SQL queries, enhancing their ability to reason about and interact with structured data. This server exposes table schemas as resources, providing column names and data types, and offers a query tool for executing read-only SQL commands. All queries are executed within a READ ONLY transaction, ensuring data integrity. By connecting your AI models to server-postgres, you unlock the ability to ground AI responses in real-time database information, enabling more accurate and context-aware interactions. Integration is straightforward, with support for both Docker and NPX deployment, making it easy to incorporate into existing MCP ecosystems. This allows developers to leverage the power of their databases within AI applications seamlessly.

Read-Only SQL Query Execution

The server-postgres MCP server allows AI models to execute read-only SQL queries against a connected PostgreSQL database. This functionality is crucial for enabling AI models to retrieve specific data from the database based on user queries or internal logic. The server enforces a READ ONLY transaction for all queries, ensuring that the AI model cannot inadvertently modify the database. This is a critical security feature, preventing unintended data corruption or unauthorized changes. For example, an AI assistant could use this feature to answer a user's question about product availability by querying a product inventory database. The AI model sends the SQL query to the server, the server executes it against the database, and the results are returned to the AI model for processing and presentation to the user. This feature leverages standard SQL syntax, making it easy to integrate with existing database schemas and AI model workflows.

Dynamic Table Schema Discovery

The server automatically discovers and exposes table schemas from the connected PostgreSQL database. This eliminates the need for manual schema definition or updates, simplifying the integration process for AI models. The schema information includes column names and data types, providing the AI model with the necessary context to construct meaningful SQL queries and interpret the results correctly. The schema information is exposed as a resource accessible via a URL (postgres://<host>/<table>/schema), allowing the AI model to programmatically retrieve and utilize the schema. For instance, an AI model could use the schema information to dynamically generate a user interface for data exploration or to validate the structure of a SQL query before execution. This dynamic discovery feature reduces development time and ensures that the AI model always has access to the most up-to-date schema information.

Secure Data Access Layer

server-postgres acts as a secure intermediary between the AI model and the PostgreSQL database. By enforcing read-only access and providing a controlled interface for data retrieval, the server mitigates the risks associated with directly exposing the database to the AI model. This architecture enhances security and data integrity, preventing unauthorized data modification or access. The server can be deployed in a secure environment, further isolating the database from external threats. For example, in a financial application, the server can be used to provide an AI-powered fraud detection system with access to transaction data without granting it direct write access to the database. This separation of concerns improves the overall security posture of the system and reduces the potential for data breaches or manipulation.

Integration via Standard Protocols

The server-postgres MCP server is designed to integrate seamlessly into the MCP ecosystem using standard protocols like HTTP/SSE. This allows for easy integration with a variety of AI models and client applications that support these protocols. The use of standard protocols ensures interoperability and reduces the complexity of integrating the server into existing infrastructure. For example, the server can be easily integrated with the Claude Desktop app by adding a simple configuration to the claude_desktop_config.json file. This allows developers to quickly and easily connect their AI models to PostgreSQL databases without having to write custom integration code. The server supports both Docker and NPX deployment options, providing flexibility in deployment and integration.