ArangoDB MCP Server

Overview

The Lucas Deangelis Arango MCP Server brings the power of ArangoDB into the Model Context Protocol ecosystem, enabling AI assistants to interact with graph and document data stores as first‑class resources. By exposing a set of declarative tools—such as , , and collection introspection utilities—the server turns arbitrary AQL queries into safe, typed operations that can be invoked directly from an AI prompt. This eliminates the need for custom connectors or middleware, allowing developers to focus on business logic rather than boilerplate database integration.

For developers building AI‑augmented applications, the server solves a common pain point: how to expose complex database functionality to an assistant without compromising security or reliability. Each tool is defined with clear input schemas, ensuring that the assistant can validate parameters before sending a request. The and tools provide dynamic discovery, letting the assistant offer context‑aware suggestions (e.g., “Which collections are available in the database?”) without hard‑coding schema details. Because the server can manage a client pool—one connection per database—it scales efficiently and avoids connection churn, which is critical for high‑throughput conversational workloads.

Key capabilities include:

• Read‑only and read/write query execution: Separate tools for safe data retrieval () versus full CRUD operations (), giving developers fine‑grained control over permissions.
• Dynamic schema exploration: and expose the database topology at runtime, enabling assistants to adapt to evolving data models.
• Resource templates: The server can expose document‑level resources () with parameterized paths, allowing the assistant to fetch specific entities by ID without writing custom code.
• Secure authentication: Credentials are supplied as command‑line arguments, keeping secrets out of configuration files while still allowing automated deployment via .

Real‑world use cases abound. A customer support chatbot could query an ArangoDB catalog to fetch product details or inventory status on demand. A data‑analysis assistant could run ad‑hoc analytics queries, returning results directly within the conversation. In an IoT scenario, the assistant might subscribe to collection changes (via future notification hooks) and alert users when new sensor data arrives. Because the MCP server conforms to Anthropic’s official format, it can be dropped into any Claude‑compatible environment with minimal friction.

In summary, the Lucas Deangelis Arango MCP Server provides a robust, secure bridge between AI assistants and ArangoDB. By offering well‑defined tools, dynamic discovery, and efficient connection management, it empowers developers to embed sophisticated graph‑and‑document operations into conversational workflows without reinventing the wheel.