Custom Discoveries TigerGraph-MCP V3.1 is a community based python Model Context Protocol server that exposes TigerGraph operations (queries, schema, vertices, edges, UDFs) as structured tools, prompts, and URI-based resources for MCP agents. This will allow you to interact with your TigerGraph Database using natural language commands!
Schema Introspection
Retrieve full graph schema (vertex & edge types).
Query View Retrieve list of available queires that your able to run
Query Execution
Run installed GSQL queries or raw GSQL strings with parameters.
Creation Vertex & Edge Create Vertices and Edges programmatically
Alter Vertex Allows for altering the Vertex to include support for Vector attributes
Upsert Vertex & Edge
Update vertices and edges attributes programmatically (this includes Vector attributes).
UDF & Algorithm Listing
Fetch installed user-defined functions and GDS algorithm catalogs.
To execute the Admin Features, your database user will need to have the database role of 'superuser'. None of the tools will show up in the /tools list if the user isn't assigned the 'superuser' role.
displayService_Status This tool displays the database service status, show which services are on-line
displayDetailed_Service_Status This tool provides all the detail that tool displayService_Status with additional information about the Graph to include vertices, edges, and installed queries
displayComponent_Version This tool will display all the versions of the components that make up TigerGraph Server
displayCPUMemory_Usage This tool will display both the CPU and Memory Usage of all the components that make up the TigerGraph Server
displayDiskSpace_Usage This tool will display all the Disk Usage of the different components that make up the TigerGraph database server.
Features++ (Kick-Ass Features include)We are including a mcp_chatbot that allow you "chat" with the database. You will need to configure two files. First, you will need to configure the mcp_server/.env file with the LLM you want to use (default is Anthropic LLM model) Second, you will need to configure the server_config.json file, under mcp_server/mcp_chatbot folder, specifying the full path to the main.py file. The chatbot usage is as follows:
Custom Discoveries TigerGraph-MCP V3.1 now includes enhanced functionality for exporting query results to CSV or JSON file formats within a designated output directory. This output directory can be configured through the mcp_server/.env file, with the default location set to ./Output.
We have developed some Session Management Features that will help the developer with handling TigerGraph Session around SECRET & TOKEN Management. In short, TigerGraph session initialization checks the following:
Determines if the database is running (ping)
Determines if the specified graph in the .env file exists, if not, it will create a graph
Checks to see if there is a Secret registered by the combination of db user name and graph name (i.e. Secret Alias), if there is no Secret Alias, the system will create a secret and token and write it out to your .env file
Once the session is authenticated it will set a TigerGraph Connection
TigerGraph-MCP/
├── installUVEnvironment.sh # Intitalizes vitural environment if one exists, if not it will
create one, and load it with the project dependencies in the
requirements.txt file.
├── LICENSE # MIT License
├── main.py # MCP app bootstrap (`run_server()`) Used to start TigerGraph_MCP server
├── chatBot_Main.py # Main python program to invoke mcp_chatbot.py
├── mcp_server
├── .env # TigerGraph (HOST, GRAPH, SECRET) & LLM configuration paramaters
├── config.py # Reads environment config file (.env) and defines System Constants
├── mcp_logger.py # Sets up the log handler and sets Logging Level to ERROR
├── agents
├── ag2 # Directory that holds AG2 Agents
├── chatAgent.py # Chatbot using AG2 agent framework for LLM
├── agent_interface.py # Interface for implementing ChatAgent
├── prompts # Directory that holds AG2 Agent prompts
├── toolKitPrompt.py # Prompts used by AG2 agent
├── toolBoxes # Directory that holds AG2 Agent functions / "tools"
├── mcpToolBox.py. # Holds the functions / "tools
├── mcp_chatbot
├── mcp_chatbot.py # Chatbot for LLM to interact with TigerGraph MCP Server (uses .env file)
├── server_config.json # Configuration file to define TigerGraph MCP Server
├── tigerGraph
├── interface.py # Interface definitions of client methods
├── mcp_Server.py # `@mcp.tool` and `@mcp.prompts` definitions, exposing client methods & prompts
├── prettyPrintDir.py # Implements pretty print directory functionality
├── services.py # Implement service calls to TigerGraph database
├── session.py # Encapsulates TigerGraphConnection and core session operations
├── Outputs # Output directory where Query outputs are written (.csv or .json format)
├── tests # test directory which containts all the tests cases written
├── README.md # ReadMe file that has all the descriptions of the test cases in the tests directory
├── pyproject.toml # Project metadata & dependencies
├── README.md # This markdown README file
├── requirements.txt # Python package dependencies
├── runChatBot.sh # Unix shell script to run TigerGraph ChatBot
└── .gitignore # Github/OS/Python ignore rules
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
MCP TigerGraph Server
The assumption is that the developer has a instance of TigerGraph running either in the cloud or on a Linux Desktop. Furthermore, it is assumed that the TigerGraph instance has a defined dba user (with valid roles and password).
Please edit your .env configuration (that you copied from the .env-exmaple) file with the following required attributes to create a TigerGraph session:
TigerGraph MCP ChatBot
The MCP ChatBot currently supports two LLMs, OPEN AI, an Anthropic claude.
You will need to update the server_config.json file (that you made a copy of) and edit the path to the run command with your full path name:
"args": ["run", "/Your full path goes here/MCP-Repository/TigerGraph-MCP/main.py"]
Once you have completed all of your configuration setup. It is now time to see the fruit of your labor! To run the Chat Bot simply invoke the script runChatBot.sh
You should see a Welcome Banner:
Welcome to Custom Discoveries TigerGraph MCP Chatbot!
Type your queries or type ['quit'|'exit'] to exit.
Use /tools to list available tools
Use /prompts to list available prompts
Query:
At the Query prompt type /tools to get a list of avaialbe tools to interactive with TigerGraph Graph database.
Connecting to Claude DesktopThis MCP server can be installed into the Claude Desktop client so that Claude can invoke your TigerGraph tools directly: See link for setting up Claude Desktop at: https://modelcontextprotocol.io/quickstart/user#2-add-the-filesystem-mcp-server
Modify the configuration file at:
git checkout -b feature/YourFeature
git commit -m "Add YourFeature"
git push origin feature/YourFeature
Please ensure all new code is covered by tests and follows PEP-8 style.
This project is licensed under the MIT License.
V3.1 is the latest release that can be found at:[https://github.com/custom-discoveries/TigerGraph_MCP/releases/tag/V3.1]
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