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TigerGraph-MCP

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TigerGraph-MCP

Custom Discoveries TigerGraph-MCP V2.1 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 naturallanguage commands!

Table of Contents

  1. Requirements
  2. Features
  3. Project Structure
  4. Installation
  5. Configuration
  6. Running Chat Bot
  7. Connecting to Claude Desktop
  8. Contributing
  9. License
  10. Releases

Requirements

  • You will need to run TigerGraph Version >= 4.2.0, either in the cloud or in a Docker Image.
  • Make sure to add a TigerGraph dba user and set your password. Set role to: 'superuser'.
  • Setup local environment with Python >= 3.12 (run the installUVEnvironment.sh script)

Features

  • Schema Introspection Retrieve full graph schema (vertex & edge types).

  • Query ViewRetrieve list of available queires that your able to run

  • Query Execution Run installed GSQL queries or raw GSQL strings with parameters.

  • Creation Vertex & EdgeCreate Vertices and Edges programmatically

  • Alter VertexAllows 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.

Features++ (Kick-Ass Features include)

  1. We are including a mcp_chatbot that allow you "chat" with the database. You will need to configuretwo 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:

    • @listQueries to see available Query Output Files
    • @<query_file_name> list content of Query file
    • /tools to list the available tools under the mcp server
    • /prompts to list the available prompts under the mcp server
    • /resources is a planned future enhancment

  2. Custom Discoveries TigerGraph-MCP V2.0 now includes enhanced functionality for exportingquery results to CSV or JSON file formats within a designated output directory. This outputdirectory can be configured through the mcp_server/.env file, with the default locationset to ./Output.

  3. We have developed some Session Management Features that will help the developer withhandling TigerGraph Session around SECRET & TOKEN Management. In short, TigerGraph sessioninitialization checks the following:

1. Determines if the database is running (ping)
2. Determines if the specified graph in the .env file exists, if not, it will create a graph
3. 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
4. Once the session is authenticated it will set a TigerGraph Connection

Project Structure

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_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
       
├── Output                    # Output directory where Query outputs are written (.csv or .json format)       
├── 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

Installation

  1. Clone the repo 
    git clone https://github.com/customdiscoveries/TigerGraph_MCP.git
cd TigerGraph-MCP
  2. Copy Example Configuration files 
    cp mcp_server/.env-example .env
cp mcp_server/mcp_chatbot/example-server_config.json server_config.json
  3. Create, Install Dependencies & Activate a virtual environment 
    installUVEnvironment.sh

Configuration Setup

MCP TigerGraph Server

The assumption is that the developer has a instance of TigerGraphrunning either in the cloud or on a Linux Desktop. Furthermore, itis 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) filewith the following required attributes to create a TigerGraph session:

  • TG_HOST=http://localhost
  • TG_GRAPH=tigerGraph_Graph_name
  • TG_USERNAME=tigerGraph_dba_user_name
  • TG_PASSWORD=tigerGraph_dba_user_password
  • TG_SECRET=(automatically captured if you don't provide one)
  • TG_TOKEN=(automatically captured if you don't provide one)

TigerGraph MCP ChatBot

The MCP ChatBot currently supports two LLMs, OPEN AI, an Anthropic claude.

  1. You will need to modify the .env (same as used in the mcp server) configurationfile and set the LLM_MODEL_FAMILY attribute.
  • LLM_MODEL_FAMILY='ANTHROPIC' # Currently supports: 'ANTHROPIC' or 'OPENAI'

  • ANTHROPIC_LLM_MODEL='claude-3-7-sonnet-20250219'
  • ANTHROPIC_API_KEY="Your Anthropic Key goes here"
  • ANTHROPIC_TOKENS=2024

  • OPENAI_LLM_MODEL='gpt-4.1-mini'
  • OPENAI_TOKENS=2024
  • OPENAI_API_KEY="Your Open AI Key goes here"

  1. You will need to update the server_config.json file (that you made a copy of) andedit the path to the run command with your full path name:

    "args": ["run", "/Your full path goes here/MCP-Repository/TigerGraph-MCP/main.py"]

Running Chat Bot

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

. 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 Desktop

This 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:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.jsonAfter configuring claude_desktop_config.json file, restart Claude Desktop and you’ll see your MCP tools available via the hammer 🛠 icon.

Contributing

  1. Fork the repository
  2. Create a feature branch 
    git checkout -b feature/YourFeature
  3. Commit your changes 
    git commit -m "Add YourFeature"
  4. Push to branch 
    git push origin feature/YourFeature
  5. Open a Pull Request

Please ensure all new code is covered by tests and follows PEP-8 style.

License

This project is licensed under the MIT License.

Releases

V2.1 is the latest release that can be found at: https://github.com/custom-discoveries/TigerGraph_MCP/releases/tag/V2.1

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