Apache AGE MCP Server

Overview

The Homebrew Age MCP Server is a lightweight, Python‑based Model Context Protocol (MCP) server that exposes the graph capabilities of Apache AGE—an extension for PostgreSQL—to AI assistants such as Claude. By turning a PostgreSQL instance that has the AGE extension enabled into an MCP endpoint, developers can give their assistants direct access to graph‑oriented queries and data modeling without leaving the familiar MCP ecosystem. This solves a common pain point: most AI assistants can query relational databases easily, but they struggle with graph‑specific operations like traversals, pattern matching, or subgraph extraction. The Age MCP Server bridges that gap by translating standard MCP requests into AGE‑specific SQL commands and returning the results in a format that Claude can understand.

The server’s value lies in its seamless integration with existing PostgreSQL deployments, especially Azure Database for PostgreSQL where AGE support is now in preview. Developers can keep their data on a managed, scalable platform while still enabling AI assistants to perform complex relationship analysis. The MCP interface abstracts the underlying SQL, allowing assistants to request operations such as “find all friends of a user” or “identify communities within a network” with simple, natural‑language prompts. The server then handles connection pooling, authentication (including support for Azure Entra ID), and query translation automatically.

Key capabilities include:

• Graph traversal and pattern matching: Execute Cypher‑style queries against AGE, enabling assistants to navigate relationships in real time.
• Schema introspection: Expose node and edge types, properties, and indexes so assistants can adapt queries dynamically.
• Secure connection handling: Support for SSL/TLS, password‑less authentication via Azure Entra ID, and environment‑based configuration.
• Extensible tooling: The server can be wrapped in additional MCP tools or prompts, allowing developers to build custom workflows that combine graph queries with other AI services.

Typical use cases span social network analysis, recommendation engines, fraud detection, and supply‑chain visibility. For example, an assistant could analyze a customer‑supplier graph to suggest new partnership opportunities, or traverse a network of transactions to flag anomalous patterns. In development pipelines, the server can be invoked from Visual Studio Code Insiders or directly from the Claude desktop client, making it a natural fit for data‑centric AI projects that rely on graph logic.

What sets the Homebrew Age MCP Server apart is its minimal footprint and tight integration with the Homebrew ecosystem. Once installed, it sits in the system PATH and can be launched from any MCP‑enabled client with a single configuration entry. This “plug‑and‑play” model eliminates the need for custom connectors or middleware, allowing teams to focus on building intelligent applications rather than wrestling with database drivers. The result is a powerful, developer‑friendly bridge that unlocks the full potential of graph data for AI assistants.