TigerGraph MCP Server

The TigerGraph‑MCP server turns a TigerGraph database into an AI‑friendly workspace. By exposing graph operations—schema queries, GSQL execution, vertex and edge manipulation, UDF listings, and administrative status checks—as structured tools, prompts, and URI‑based resources, it lets an AI assistant like Claude issue natural‑language commands that translate directly into graph queries or maintenance tasks. This eliminates the need for developers to write raw GSQL code or manually interact with TigerGraph’s REST endpoints, streamlining the development cycle and enabling rapid prototyping.

At its core, the server offers schema introspection to fetch full vertex and edge definitions, a query view that lists all installed GSQL scripts, and an execution engine capable of running both predefined queries and arbitrary GSQL strings with parameters. Programmatic creation, alteration (including vector attributes), and upsert of vertices and edges are all available as discrete tools, allowing an AI to modify the graph on the fly. UDF and algorithm catalogs are also surfaced so that custom functions can be invoked without leaving the AI interface.

For administrators, the MCP provides a suite of privileged tools—displaying service status, detailed component health, version information, CPU/memory usage, and disk space. These are gated behind the role to safeguard sensitive operations. The server also ships a lightweight chatbot that lets users converse with the database via commands such as or , making it easy to discover capabilities and iterate on queries in real time.

Developers benefit from several standout features: an export system that writes query results to CSV or JSON in a configurable output directory, session management utilities for handling secrets and tokens securely, and an extensible plugin architecture that can be expanded with custom prompts or tools. By integrating directly into AI workflows, the TigerGraph‑MCP server turns complex graph operations into conversational tasks, accelerating data science projects, troubleshooting, and operational monitoring in a single, cohesive environment.