GraphQL MCP Server

A lightweight, experimental Rust server that connects your GraphQL data API to LLMs.
Ideal for rapid prototyping, experimentation, and learning.

This project provides:

• Three integration approaches:
  • A1: Custom engineered queries as tools
  • A2: Full schema supplied in LLM context (best for smaller schemas)
  • A3: Agentic tool discovery and query execution
• A benchmarking environment to measure token usage, tool calls, latency, and accuracy

See the relevant sections below for benchmarking and best practices.

Use this MCP server as an abstraction layer between your API endpoint and LLMs:

Overview

This project provides a GraphQL MCP server implemented in Rust, as well as test servers for local development:

• High performance through Rust.
• Modular: Easily connect to custom GraphQL endpoints using different exploration strategies.
• Flexible: Choose between Discovery or Fullschema exploration at runtime.
• Ideal for experimentation and learning: Observe live tool calls, responses, and server behavior.

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Services

• graphql-mcp: Main GraphQL MCP server. Run via Docker or Cargo. Forwards calls to a specified GraphQL endpoint.
• test-graphql-server: Mock GraphQL endpoint for testing MCP integration.
• test-llm-server: Mock LLM server for end-to-end prototyping and local dev.

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Prerequisites

• Rust & Cargo (required)
• Docker (optional, for containerized deployment)

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Setup & Getting Started

1. Clone the Repository

git clone https://github.com/ThoreKoritzius/graphql-mcp.git
cd graphql-mcp

2. Setup Environment

Create a .env file in the root directory with:

OPENAI_API_KEY=your_openai_api_key

3. Run with Cargo

Run the server, specifying your GraphQL endpoint and exploration strategy:

# Discovery strategy (default)
cargo run -- --endpoint http://127.0.0.1:3000 --strategy discovery

# Fullschema strategy
cargo run -- --endpoint http://127.0.0.1:3000 --strategy fullschema

The server will be available at: http://localhost:5001

Note: The strategy flag controls whether the server uses the lightweight Discovery explorer (field-by-field introspection) or the Fullschema explorer (fetches the entire schema at once).

4. (Optional, Easy Full Setup) Or test the full setup - Run with Docker Compose

Launch all services with:

docker-compose up --build

When all services are running, visit http://localhost:3000/ for a demo chat UI with live tool calls and answers.

Control your ednpoint and strategy via the docker compose environment variables

services:
  graphql-mcp:
    environment:
      - RUST_LOG=debug
      - ENDPOINT=http://test-graphql-server:8000 # or your desired GraphQL endpoint
      - STRATEGY=discovery # or 'fullschema'

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Usage

• The main server (graphql-mcp) listens on port 5001 by default.
• To connect your own LLM service (e.g., Claude, OpenAI, etc.), configure your MCP client:

{
  "mcpServers": {
    "graphql_mcp": {
      "url": "http://graphql-mcp-server:5001/sse"
    }
  }
}

• Tools registered by the server are automatically exposed for the chosen strategy:
  • Discovery: field-by-field inspection, SDL snippets, and query execution.
  • Fullschema: complete schema introspection and query execution.

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Configuration

• Set GraphQL endpoint with --endpoint http://your-endpoint
• Select exploration strategy with --strategy discovery|fullschema
• Set your OpenAI API key in .env
• Exposed ports:
  • 5001 (GraphQL MCP server)
  • 3000 (Demo UI)

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Inspector Integration (Optional)

To introspect and debug MCP traffic live, use the MCP Inspector:

npx @modelcontextprotocol/inspector http://localhost:5001/sse

• Select Transport Type: SSE
• This enables live introspection and visualization of MCP traffic.

Blueprint for Tool Selection

A1 Custom Tools

• Provide custom tools for often used or complex queries
• Provide a clear tool description.

A2 Full Schema

• Load full schema in context
• Should be used only for small schemas (due to context-size limitations and degraded accuracy with increased context)
• Make sure to use Key-Value LLM Caching APIs to reduce input token cost

A3 Agentic Discovery

• Agentic Schema Discovery improves with rich context
• Use type annotations and documentation of your schema.
• Include examples of working queries.

Benchmarks

Benchmark scripts for the three strategies are located in the bench folder. To run benchmarks, simply bring up the required services using Docker Compose:

docker compose up

and then run the bench.py script, which runs the dataset.csv throguh the test-client and captures results and graphs.

Configuration Example:

For the main and control services, you can configure the following environment variables:

services:
  graphql-mcp:
    environment:
      - RUST_LOG=debug
      - ENDPOINT=http://test-graphql-server:8000 # or your desired GraphQL endpoint
      - STRATEGY=discovery # or 'fullschema'

We benchmarked our server using all three strategies on the SpaceDevs Example Schema and our benchmark dataset.

Results: