EdgeDB MCP Server

The EdgeDB MCP Server is a purpose‑built bridge that exposes the full power of an EdgeDB database to AI assistants through the Model Context Protocol. By translating MCP requests into native EdgeQL operations, it allows conversational agents to perform sophisticated data queries, schema introspection, and database administration without leaving the chat interface. This capability turns a simple chatbot into a dynamic data‑driven assistant capable of answering real business questions, generating reports, or even automating deployment workflows.

At its core, the server offers three complementary layers of functionality. First, database management tools let users connect to any EdgeDB instance or DSN, list available databases, create new ones, and switch the active context on demand. Second, query tools provide a rich set of EdgeQL execution primitives: arbitrary queries, parameterised calls, single‑record fetches, and paginated multi‑record retrievals with filtering, sorting, and pagination controls. Finally, schema management tools expose introspection endpoints to list types, fetch detailed type definitions, and compare schema versions—enabling assistants to explain database structure or detect drift between environments.

Developers can integrate the server either as a standalone command‑line utility or as a library inside their own Node.js applications. In a conversational workflow, an AI assistant can request the server to run a query, receive structured JSON results, and then weave those results into a natural‑language response. For example, a sales analyst could ask the assistant to “show me the top 10 customers by revenue in Q3,” and the MCP server would translate that into a single EdgeQL statement, execute it against the connected database, and return the data for the assistant to format.

Unique advantages of this MCP server include its zero‑code integration with existing EdgeDB deployments, native support for DSN or instance name configuration, and a lightweight resource‑and‑tool registration system that keeps the protocol surface minimal yet expressive. The server’s debug and log level environment variables give operators fine‑grained control over observability, while the modular directory structure encourages easy extension with custom tools or resources. In practice, teams building AI‑powered data dashboards, automated audit scripts, or intelligent documentation generators can plug this server into their workflows to unlock instant, schema‑aware data access without building custom adapters.