TigerGraph MCP Server is a community Python MCP server exposing TigerGraph graph-database operations as structured tools: schema introspection, GSQL query listing and execution, vertex/edge creation and updates (including vector attributes), user-defined function and GDS algorithm catalog listing, and administrative service monitoring. It also ships a built-in chat-bot with session management that automatically handles TigerGraph's SECRET and TOKEN retrieval, and supports both Anthropic Claude and OpenAI models for that chat interface.

Use this connector when an agent needs to query or modify a TigerGraph graph database through natural language - inspecting the schema, running installed GSQL queries or raw GSQL strings with parameters, creating or updating vertices and edges, or listing available algorithms. Administrative tools (displayService_Status, displayComponent_Version, displayCPUMemory_Usage, and related monitoring tools) require the database's superuser role, so they are not available to a regularly-scoped database user. It requires TigerGraph version 4.2.0 or higher and Python 3.12 or higher, so older TigerGraph deployments are out of scope.

schema_introspection retrieves the complete vertex and edge type schema; query_view lists available queries and query_execution runs installed GSQL queries or raw GSQL with parameters. create_vertex_edge and upsert_vertex_edge create and update vertices/edges programmatically, including vector attributes, while alter_vertex enables vector-attribute support on an existing vertex type. udf_algorithm_listing surfaces installed user-defined functions and the GDS algorithm catalog. Query results can be exported to CSV or JSON.

git clone https://github.com/customdiscoveries/TigerGraph_MCP.git
cd TigerGraph-MCP
cp mcp_server/.env-example .env
cp mcp_server/mcp_chatbot/example-server_config.json server_config.json
installUVEnvironment.sh

Required environment variables are TG_HOST, TG_GRAPH, TG_USERNAME, TG_PASSWORD, and LLM_MODEL_FAMILY (ANTHROPIC or OPENAI); TG_SECRET and TG_TOKEN are auto-retrieved if not set, and the corresponding ANTHROPIC_API_KEY/OPENAI_API_KEY (plus model name and token limit variables) configure the chat-bot's LLM. Claude Desktop configuration points at the cloned main.py via its full path.

Developers and data teams working with TigerGraph who want natural-language access to schema inspection, query execution, and graph data mutation - including vector-attribute workflows for similarity search - plus database administrators who need superuser-gated visibility into service status, component versions, and resource usage across the TigerGraph cluster. Its built-in chat-bot mode, with commands like @listQueries to browse query result files, /tools to list available MCP tools, and /prompts to list available prompts, gives a lighter-weight interactive path for exploring a graph before wiring the same tools into a larger agent workflow, and query results can be exported to CSV or JSON for further analysis outside the chat session.