Enables AI agents to interact with TigerGraph databases using the Model Context Protocol (MCP) standard.
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The pyTigerGraph-mcp tool provides a Model Context Protocol (MCP) server, allowing AI agents and Large Language Models (LLMs) to seamlessly interact with TigerGraph databases. It leverages pyTigerGraph's asynchronous APIs for optimal performance, offering a standardized and LLM-friendly way to perform various graph operations, schema management, data loading, and even GSQL/openCypher generation within a TigerGraph environment. It integrates with popular AI agent frameworks like LangGraph, CrewAI, and GitHub Copilot Chat, simplifying the development of intelligent graph-powered applications.
Key Features
01Comprehensive suite of tools for graph, schema, data, query, and vector operations.
02LLM-friendly structured JSON responses for easy parsing and error handling.
03Rich tool descriptions with usage examples, workflows, and related tools for AI agent understanding.
04Optimized token usage in responses for efficient LLM interaction.
05Support for multiple TigerGraph connection profiles and programmatic connection management.
060 GitHub stars
Use Cases
01Building stateful, agent-based workflows with LangGraph for complex graph interactions.
02Creating agentic workflows with CrewAI for simpler automation and web-based UI integration.
03Performing quick tasks or direct tool invocations using GitHub Copilot Chat within a development environment.
