Dgraph MCP Server

Dgraph MCP Server

The Dgraph MCP Server bridges large‑language‑model (LLM) applications and the Dgraph graph database through the Model Context Protocol. By exposing a set of tools that wrap common Dgraph operations—querying, mutating, and schema management—it lets an AI assistant treat the graph as a first‑class data source without writing custom drivers or SDKs. This eliminates the need for developers to embed database logic directly in their prompts, enabling more natural, conversational interactions with structured data.

At its core, the server implements three actionable tools:

• executes DQL statements and returns results in JSON, allowing the assistant to retrieve complex graph patterns or aggregate data.
• performs RDF mutations, optionally committing the transaction, so the assistant can create or update nodes and edges on demand.
• lets the model modify the graph schema, adding or changing predicates and indices dynamically. In addition, a resource provides the current schema snapshot for introspection. These capabilities give developers fine‑grained control over graph data while keeping the interaction surface minimal and declarative.

Developers benefit from this server in several practical scenarios. In knowledge‑base applications, an assistant can pull entity relationships or update user profiles in real time. For recommendation engines, the model can query friend networks or product similarity graphs and then write new preference edges. In data‑driven storytelling, the assistant can traverse a narrative graph, fetch character attributes, and augment the storyline with fresh nodes—all without exposing database credentials or complex query syntax to end users. The server’s reliance on standard input/output makes it agnostic to the LLM framework, so it can be dropped into any system that supports MCP, from Claude’s local runtime to third‑party orchestration layers.

What sets this implementation apart is its tight coupling with the mcp-go library, which guarantees compliance with MCP specifications and provides robust error handling. The server also offers environment‑driven configuration, simplifying deployment across local dev environments and cloud deployments where Dgraph may run in a managed cluster. By abstracting the intricacies of Dgraph’s transaction model and schema language, the MCP server empowers developers to focus on higher‑level business logic while giving AI assistants direct, secure access to graph data.