Text-to-GraphQL MCP Server

Transform natural language queries into GraphQL queries using an MCP (Model Context Protocol) server that integrates seamlessly with AI assistants like Claude Desktop and Cursor.

🚀 Overview

The Text-to-GraphQL MCP Server converts natural language descriptions into valid GraphQL queries using an AI agent built with LangGraph. It provides a bridge between human language and GraphQL APIs, making database and API interactions more intuitive for developers and non-technical users alike.

✨ Features

• Natural Language to GraphQL: Convert plain English queries to valid GraphQL
• Schema Management: Load and introspect GraphQL schemas automatically
• Query Validation: Validate generated queries against loaded schemas
• Query Execution: Execute queries against GraphQL endpoints with authentication
• Query History: Track and manage query history across sessions
• MCP Protocol: Full compatibility with Claude Desktop, Cursor, and other MCP clients
• Error Handling: Graceful error handling with detailed debugging information
• Caching: Built-in caching for schemas and frequently used queries

🛠 Installation

Prerequisites: Install UV (Recommended)

UV is a fast Python package installer and resolver. Install it first:

macOS/Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows:

powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Find your UV installation path:

# Find where uv is installed
which uv

# Common locations:
# macOS/Linux: ~/.local/bin/uv
# Windows: %APPDATA%\uv\bin\uv.exe

Important: You'll need the UV path for MCP configuration. The typical path is ~/.local/bin on macOS/Linux, which translates to /Users/yourusername/.local/bin (replace yourusername with your actual username).

Setup for MCP Usage

# Clone the repository
git clone https://github.com/Arize-ai/text-to-graphql-mcp.git
cd text-to-graphql-mcp

# Install dependencies (UV automatically creates virtual environment)
uv sync

# Test the installation
uv run text-to-graphql-mcp --help

Note: The uv run pattern automatically handles virtual environments, making MCP configuration cleaner and more reliable than traditional pip installations.

Alternative Installation Methods

From PyPI (when published):

pip install text-to-graphql-mcp

Development Setup:

# For contributing to the project
uv sync --dev

🏃‍♂️ Quick Start

1. Configure with Cursor (Recommended)

Add to your .cursor/mcp.json:

{
  "text-to-graphql": {
    "command": "uv",
    "args": [
      "--directory",
      "/path/to/text-to-graphql-mcp",
      "run",
      "text-to-graphql-mcp"
    ],
    "env": {
      "PATH": "/path/to/uv/bin:/usr/bin:/bin",
      "OPENAI_API_KEY": "your_openai_api_key_here",
      "GRAPHQL_ENDPOINT": "https://your-graphql-api.com/graphql",
      "GRAPHQL_API_KEY": "your_api_key_here",
      "GRAPHQL_AUTH_TYPE": "bearer"
    }
  }
}

Important Setup Notes:

• Replace /path/to/text-to-graphql-mcp with the actual path to your cloned repository
• Replace /path/to/uv/bin with your actual UV installation path (typically /Users/yourusername/.local/bin on macOS)
• The PATH environment variable is required for MCP clients to find the uv command

2. Configure with Claude Desktop

Add to your Claude Desktop MCP configuration file:

macOS:~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:%APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "text-to-graphql": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/text-to-graphql-mcp",
        "run",
        "text-to-graphql-mcp"
      ],
      "env": {
        "PATH": "/path/to/uv/bin:/usr/bin:/bin",
        "OPENAI_API_KEY": "your_openai_api_key_here",
        "GRAPHQL_ENDPOINT": "https://your-graphql-api.com/graphql",
        "GRAPHQL_API_KEY": "your_api_key_here",
        "GRAPHQL_AUTH_TYPE": "bearer"
      }
    }
  }
}

Setup Instructions:

1. Find your UV path: Run which uv in terminal (typically /Users/yourusername/.local/bin/uv)
2. Set the PATH: Use the directory containing uv (e.g., /Users/yourusername/.local/bin)
3. Replace paths: Update both the --directory argument and PATH environment variable with your actual paths
4. Add your API keys: Replace the placeholder values with your actual API keys

3. Common UV Path Examples

# Find your UV installation
which uv

# Common paths by OS:
# macOS: /Users/yourusername/.local/bin/uv
# Linux: /home/yourusername/.local/bin/uv  
# Windows: C:\Users\yourusername\AppData\Roaming\uv\bin\uv.exe

# For MCP config, use the directory path:
# macOS: /Users/yourusername/.local/bin
# Linux: /home/yourusername/.local/bin
# Windows: C:\Users\yourusername\AppData\Roaming\uv\bin

4. Alternative: Use Environment Variables

If you prefer using a .env file (useful for local development):

# Required
OPENAI_API_KEY=your_openai_api_key_here
GRAPHQL_ENDPOINT=https://your-graphql-api.com/graphql
GRAPHQL_API_KEY=your_api_key_here

# Optional - Authentication method (bearer|apikey|direct)
GRAPHQL_AUTH_TYPE=bearer

# Optional - Model settings
MODEL_NAME=gpt-4o
MODEL_TEMPERATURE=0

Then use a simplified MCP configuration (still requires PATH):

{
  "text-to-graphql": {
    "command": "uv",
    "args": [
      "--directory",
      "/path/to/text-to-graphql-mcp",
      "run",
      "text-to-graphql-mcp"
    ],
    "env": {
      "PATH": "/path/to/uv/bin:/usr/bin:/bin"
    }
  }
}

5. Run the MCP Server (Optional - for testing)

# Run the server directly for testing
text-to-graphql-mcp

# Or run as a module
python -m text_to_graphql_mcp.mcp_server

🔧 Usage

Available MCP Tools

generate_graphql_query

Convert natural language to GraphQL queries.

Input: "Get all users with their names and emails"
Output: query { users { id name email } }

validate_graphql_query

Validate GraphQL queries against the loaded schema.

execute_graphql_query

Execute GraphQL queries and return formatted results.

get_query_history

Retrieve the history of all queries in the current session.

get_query_examples

Get example queries to understand the system's capabilities.

Example Interactions

Natural Language Input:

"Show me all blog posts from the last week with their authors and comment counts"

Generated GraphQL:

query {
  posts(where: { createdAt: { gte: "2024-06-05T00:00:00Z" } }) {
    id
    title
    content
    createdAt
    author {
      id
      name
      email
    }
    comments {
      id
    }
    _count {
      comments
    }
  }
}

🐳 Deploying with Docker

💡 Key Concept: When using Docker with MCP clients (Claude/Cursor), environment variables are set during container startup (docker run), not in the MCP client configuration. The MCP clients simply connect to the already-running container.

Building the Docker Image

# Clone the repository
git clone https://github.com/Arize-ai/text-to-graphql-mcp.git
cd text-to-graphql-mcp

# Build the Docker image
docker build -t text-to-graphql-mcp .

Running the Container

Method 1: Using Environment Variables Directly

docker run -d \
  --name text-to-graphql-mcp \
  -p 8000:8000 \
  -e OPENAI_API_KEY="your_openai_api_key_here" \
  -e GRAPHQL_ENDPOINT="https://your-graphql-api.com/graphql" \
  -e GRAPHQL_API_KEY="your_api_key_here" \
  -e GRAPHQL_AUTH_TYPE="bearer" \
  -e MODEL_NAME="gpt-4o" \
  text-to-graphql-mcp

Method 2: Using an Environment File

Create a .env file:

OPENAI_API_KEY=your_openai_api_key_here
GRAPHQL_ENDPOINT=https://your-graphql-api.com/graphql
GRAPHQL_API_KEY=your_api_key_here
GRAPHQL_AUTH_TYPE=bearer
MODEL_NAME=gpt-4o
MODEL_TEMPERATURE=0

Run the container:

docker run -d \
  --name text-to-graphql-mcp \
  -p 8000:8000 \
  --env-file .env \
  text-to-graphql-mcp

Method 3: Using Docker Compose

Create a docker-compose.yml file:

version: '3.8'

services:
  text-to-graphql-mcp:
    build: .
    container_name: text-to-graphql-mcp
    ports:
      - "8000:8000"
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - GRAPHQL_ENDPOINT=${GRAPHQL_ENDPOINT}
      - GRAPHQL_API_KEY=${GRAPHQL_API_KEY}
      - GRAPHQL_AUTH_TYPE=${GRAPHQL_AUTH_TYPE:-bearer}
      - MODEL_NAME=${MODEL_NAME:-gpt-4o}
      - MODEL_TEMPERATURE=${MODEL_TEMPERATURE:-0}
      - API_HOST=0.0.0.0  # Important: bind to all interfaces in container
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3

Then run:

# Start the service
docker-compose up -d

# View logs
docker-compose logs -f

# Stop the service
docker-compose down

Using Docker with MCP Clients

When running the MCP server in Docker, you need to use docker exec to communicate with the container:

Important: The environment variables (OPENAI_API_KEY, GRAPHQL_ENDPOINT, etc.) must be set when you first run the container using one of the methods above. The MCP client configurations below only connect to an already-running container.

Step 1: First, ensure your container is running with environment variables

# Example: Make sure the container is running with your environment variables
docker run -d \
  --name text-to-graphql-mcp \
  -p 8000:8000 \
  --env-file .env \
  text-to-graphql-mcp

# Verify the container is running
docker ps | grep text-to-graphql-mcp

Step 2: Configure Cursor

Add to .cursor/mcp.json:

{
  "text-to-graphql": {
    "command": "docker",
    "args": [
      "exec",
      "-i",
      "text-to-graphql-mcp",
      "uv",
      "run",
      "python",
      "-m",
      "src.text_to_graphql_mcp.mcp_server"
    ]
  }
}

Step 2: Configure Claude Desktop

Add to your Claude Desktop configuration:

{
  "mcpServers": {
    "text-to-graphql": {
      "command": "docker",
      "args": [
        "exec",
        "-i",
        "text-to-graphql-mcp",
        "uv",
        "run",
        "python",
        "-m",
        "src.text_to_graphql_mcp.mcp_server"
      ]
    }
  }
}

Note: The MCP client configurations don't need environment variables because they're connecting to a container that already has them set. If you restart the container, make sure to include the environment variables again.

🏗 Architecture

The system uses a multi-agent architecture built with LangGraph:

1. Intent Recognition: Understands what the user wants to accomplish
2. Schema Management: Loads and manages GraphQL schema information
3. Query Construction: Builds GraphQL queries from natural language
4. Query Validation: Ensures queries are valid against the schema
5. Query Execution: Executes queries against the GraphQL endpoint
6. Data Visualization: Provides recommendations for visualizing results

⚙️ Configuration

Environment Variables

Authentication Types

• bearer (default): Uses Authorization: Bearer <token> - standard for most GraphQL APIs
• apikey: Uses X-API-Key: <key> - used by some APIs like Arize
• direct: Uses Authorization: <token> - direct token without Bearer prefix
• Custom: Set GRAPHQL_HEADERS to override with any custom authentication format

Common GraphQL API Examples

GitHub GraphQL API:

GRAPHQL_ENDPOINT=https://api.github.com/graphql
GRAPHQL_API_KEY=ghp_your_github_personal_access_token
GRAPHQL_AUTH_TYPE=bearer

Shopify GraphQL API:

GRAPHQL_ENDPOINT=https://your-shop.myshopify.com/admin/api/2023-10/graphql.json
GRAPHQL_API_KEY=your_shopify_access_token
GRAPHQL_AUTH_TYPE=bearer

Arize GraphQL API:

GRAPHQL_ENDPOINT=https://app.arize.com/graphql
GRAPHQL_API_KEY=your_arize_developer_api_key
# Auth type auto-detected for Arize

Hasura:

GRAPHQL_ENDPOINT=https://your-app.hasura.app/v1/graphql
GRAPHQL_HEADERS={"x-hasura-admin-secret": "your_admin_secret"}

🔍 Observability & Agent Development

Want to build better AI agents quickly? Check out Arize Phoenix - an open-source observability platform specifically designed for LLM applications and agents. Phoenix provides:

• Real-time monitoring of your agent's performance and behavior
• Trace visualization to understand complex agent workflows
• Evaluation frameworks for testing and improving agent responses
• Data quality insights to identify issues with your training data
• Cost tracking for LLM API usage optimization

Phoenix integrates seamlessly with LangChain and LangGraph (which this project uses) and can help you:

• Debug agent behavior when queries aren't generated correctly
• Monitor GraphQL query quality and success rates
• Track user satisfaction and query complexity
• Optimize your agent's prompt engineering

Get started with Phoenix:

pip install arize-phoenix
phoenix serve

Visit docs.arize.com/phoenix for comprehensive guides on agent observability and development best practices.

🧪 Development

Setup Development Environment

# Install development dependencies
uv pip install -e ".[dev]"

# Run tests
pytest

# Format code
black .
isort .

# Type checking
mypy src/

Project Structure

text-to-graphql-mcp/
├── src/text_to_graphql_mcp/     # Main package
│   ├── mcp_server.py            # MCP server implementation
│   ├── agent.py                 # LangGraph agent logic
│   ├── config.py                # Configuration management
│   ├── logger.py                # Logging utilities
│   ├── tools/                   # Agent tools
│   └── ...
├── tests/                       # Test suite
├── docs/                        # Documentation
├── pyproject.toml              # Package configuration
└── README.md

🤝 Contributing

We welcome contributions! Please see our contributing guidelines for details.

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

📝 License

This project is licensed under the Elastic License 2.0 (ELv2) - see the LICENSE file for details.

🐛 Troubleshooting

Common Issues

"No module named 'text_to_graphql_mcp'"

• Ensure you've installed the package: pip install text-to-graphql-mcp

"OpenAI API key not found"

• Set your OPENAI_API_KEY environment variable
• Check your .env file configuration

"GraphQL endpoint not reachable"

• Verify your GRAPHQL_ENDPOINT URL
• Check network connectivity and authentication

"Schema introspection failed"

• Ensure the GraphQL endpoint supports introspection
• Check authentication headers if required

🔗 Links

• Issues & Bug Reports
• MCP Protocol Documentation

🙏 Acknowledgments

• Built with LangChain and LangGraph
• Uses FastMCP for MCP server implementation