text-to-graphql-mcp 0.1.0

Transform natural language queries into GraphQL queries using an MCP server

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 advanced AI agents 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"

Setup for MCP Usage

# 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": {
      "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"
    }
  }
}

Replace /path/to/text-to-graphql-mcp with the actual path to your cloned repository.

2. Configure with Claude Desktop

Add to your Claude Desktop MCP configuration:

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

3. 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:

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

4. 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
    }
  }
}

🏗 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

Variable	Description	Default
OPENAI_API_KEY	OpenAI API key for LLM operations	Required
GRAPHQL_ENDPOINT	GraphQL API endpoint URL	Required
GRAPHQL_API_KEY	API key for your GraphQL service	Required
GRAPHQL_AUTH_TYPE	Authentication method: bearer, apikey, or direct	bearer
GRAPHQL_HEADERS	Custom headers as JSON (overrides auto-auth)	{}
MODEL_NAME	OpenAI model to use	gpt-4o
MODEL_TEMPERATURE	Model temperature for responses	0
API_HOST	Server host address	127.0.0.1
API_PORT	Server port	8000
RECURSION_LIMIT	Max recursion for agent workflow	10

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

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