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Use natural language to query TigerGraph, run GSQL, and manage graph data.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "tigergraph-mcp" yet — see the docs or source repo.
Connect to TigerGraph, list the current graph's vertex types, edge types, and key attributes, then briefly explain their relationships in English.
A schema overview of the graph database, including vertices, edges, attributes, and relationship notes.
Write and run a GSQL query on the user relationship graph to find the top 10 most active users within two hops of user A, and explain the result fields.
A valid executed GSQL query, the returned results, and an explanation of the output fields.
Using the transaction graph in TigerGraph, analyze which accounts may be central to suspicious fund flows, provide reasoning, and suggest follow-up graph queries.
Graph-based anomaly findings, supporting reasoning, and recommended follow-up queries.
Connect AI to GitHub via MCP for repository management and developer workflows.
Connect modular skill packages to any LLM via MCP for specialized workflows.
Enable AI agents to query knowledge graphs and delegate shell tasks efficiently.
Access Tiger Cloud services, databases, and docs for development and operations.
Ingest documents into Neo4j to build and query a knowledge graph.
Turn natural language into PostgreSQL queries with readable, context-aware answers.