What is Tg Mcp

TG_MCP is a lightweight Python interface that provides structured tools and URI-based resources for MCP agents to perform various TigerGraph operations, including queries, schema management, vertices, edges, and user-defined functions (UDFs).

Use cases

Use cases for TG_MCP include executing complex graph queries, programmatically managing graph data (vertices and edges), and integrating TigerGraph functionalities into larger applications or workflows.

How to use

To use TG_MCP, clone the repository, set up a virtual environment, install the necessary dependencies, and configure the required environment variables. After that, you can execute queries, manage graph objects, and access resources using the provided URIs.

Key features

Key features of TG_MCP include schema introspection, query execution, vertex and edge upsert capabilities, resource URIs for accessing graph objects, and listing of user-defined functions and algorithms.

Where to use

TG_MCP can be used in various fields such as data science, machine learning, and any application that requires graph database operations, particularly those leveraging TigerGraph’s capabilities.

TG_MCP

A lightweight Python interface that exposes TigerGraph operations (queries, schema, vertices, edges, UDFs) as structured tools and URI-based resources for MCP agents.

Table of Contents

1. Features
2. Project Structure
3. Installation
4. Configuration
5. Connecting to Claude
6. Examples
7. Contributing
8. License

Features

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

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

• Vertex & Edge Upsert
  Create or update vertices and edges programmatically.

• Resource URIs
  Access graph objects through tgraph://vertex/... and tgraph://query/... URIs.

• UDF & Algorithm Listing
  Fetch installed user-defined functions and GDS algorithm catalogs.

Project Structure

TG_MCP/
├── config.py            # Environment config (HOST, GRAPH, SECRET)
├── tg_client.py         # Encapsulates TigerGraphConnection and core operations
├── tg_tools.py          # `@mcp.tool` definitions exposing client methods
├── tg_resources.py      # `@mcp.resource` URI handlers
├── main.py              # MCP app bootstrap (`mcp.run()`)
├── pyproject.toml       # Project metadata & dependencies
├── LICENSE              # MIT License
└── .gitignore           # OS/Python ignore rules

Installation

1. Clone the repo

   git clone https://github.com/Muzain187/TG_MCP.git
   cd TG_MCP
   

2. Create & activate a virtual environment

   python3 -m venv venv
   source venv/bin/activate
   

3. Install dependencies

   pip install .
   

   Requires mcp[cli]>=1.6.0 and pyTigerGraph>=1.8.6.

Configuration

Set the following environment variables before running:

export TG_HOST=https://<your-tigergraph-host>
export TG_GRAPH=<your-graph-name>
export TG_SECRET=<your-api-secret>

These are read by config.py.

Connecting to Claude

This MCP server can be installed into the Claude Desktop client so that Claude can invoke your TigerGraph tools directly:

uv run mcp install main.py

After running the above, restart Claude Desktop and you’ll see your MCP tools available via the hammer 🛠 icon.

Examples:

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.