Neo4j MCP Chainlit

Neo4J MCP Chainlit is a proof‑of‑concept that stitches together three powerful components—Neo4j’s Model Context Protocol, Chainlit’s interactive web UI, and Anthropic’s Claude LLM—to deliver a natural‑language graph query experience. The server exposes Neo4j as an MCP resource, allowing Claude to read and write graph data through Cypher queries while Chainlit handles the conversational front‑end. This integration eliminates the need for developers to write custom adapters or manually translate user intent into Cypher, thereby accelerating prototyping and experimentation with graph‑aware AI assistants.

The core value of this MCP server lies in its ability to turn any Neo4j database into a conversational knowledge base. Developers can simply configure the MCP connection in Chainlit, point it at a Neo4j instance (including hosted demos), and start asking questions such as “Which movies did Tom Hanks act in?” or “Show me the relationship between actors and directors.” Claude parses the natural‑language request, formulates a Cypher query, and returns results that are rendered in the chat UI. This workflow keeps the logic of intent extraction, query generation, and result formatting encapsulated within the MCP server, freeing developers to focus on higher‑level application logic.

Key capabilities include:

• Model Context Protocol integration – Neo4j’s MCP driver exposes a clean API for reading, writing, and streaming graph data to LLMs.
• Chainlit web interface – Provides a real‑time, multi‑user chat experience with built‑in support for configuring MCP connections on the fly.
• Claude LLM orchestration – Handles natural‑language understanding and Cypher generation, leveraging Anthropic’s powerful reasoning abilities.
• Demo database support – Out‑of‑the‑box access to the Movie Graph demo, enabling instant experimentation without setting up a local Neo4j instance.

Typical use cases span data discovery in enterprise knowledge graphs, conversational analytics dashboards, and AI‑driven recommendation engines. For example, a product manager can ask “What features are most requested by users of Product X?” and receive a concise graph‑based answer without writing SQL or Cypher. Similarly, data scientists can prototype exploratory queries through conversation before committing to production code.

By unifying MCP, Chainlit, and Claude, this server offers a streamlined path from data to dialogue. Developers benefit from reduced boilerplate, consistent API contracts, and the flexibility to swap out LLMs or graph backends with minimal friction. The result is a versatile platform that turns complex graph queries into intuitive conversational interactions, accelerating innovation in data‑centric AI applications.