Cursor MCP: How to Use MCP Servers with Cursor AI IDE

Introduction to Cursor AI and MCP

Cursor MCP (Model Control Protocol) represents a significant advancement in the integration of large language models (LLMs) with modern code editing environments. This protocol enables developers to connect the Cursor editor with custom model servers, allowing for flexible model selection, inference optimization, and advanced customization of AI-assisted development workflows. By leveraging MCP, developers can bypass the limitations of hosted model APIs, implement custom prompt engineering techniques, and configure model parameters to suit specific coding tasks. This article provides a comprehensive technical exploration of Cursor MCP implementation, configuration, and optimization strategies for advanced users seeking to maximize the potential of local and remote model servers within their development environment.

GitHub Repository: github.com/getcursor/cursor-mcp (opens in a new tab)

Technical Architecture of Cursor MCP

Protocol Specifications

Cursor MCP implements a WebSocket-based communication layer that facilitates bi-directional data exchange between the editor client and model servers. The protocol employs a JSON-RPC 2.0 compliant messaging structure with several extensions for handling streaming responses, cancellation signals, and model-specific metadata. Primary message types include:

Request Messages: Contains model parameters, context window data, and query specifications
Response Messages: Streaming or complete model outputs with token probability distributions
Control Messages: Cancellation signals, heartbeats, and session management commands
Metadata Exchange: Model capabilities, version information, and parameter constraints

The protocol incorporates binary message compression using MessagePack for large context windows, significantly reducing network overhead when transmitting code context to the inference server.

Server Components

A typical MCP server implements the following components:

├── inference_manager/
│   ├── model_loader.py          # Dynamic model loading and unloading
│   ├── request_scheduler.py     # Request prioritization and queuing
│   └── cache_manager.py         # Response caching for repeated queries
├── protocol/
│   ├── websocket_handler.py     # WebSocket connection management
│   ├── message_serializer.py    # Protocol message serialization
│   └── session_manager.py       # Client session tracking
├── models/
│   ├── model_registry.py        # Available model registration
│   └── inference_wrapper.py     # Common interface for different models
└── server.py                    # Main server entry point

The modular design allows for plugging in different model backends, from local quantized models to remote API proxies, while maintaining a consistent interface for the Cursor client.

Setting Up Your MCP Server

Prerequisites

Before implementing an MCP server, ensure your environment meets these requirements:

Python 3.9+ with virtual environment support
At least 16GB RAM for small/medium models (32GB+ recommended)
CUDA-compatible GPU with 8GB+ VRAM for reasonable performance
Sufficient disk space for model weights (ranging from 4GB to 50GB)
High-bandwidth network connection for remote client connections

Installation Process

Clone the MCP reference implementation:

git clone https://github.com/getcursor/cursor-mcp
cd cursor-mcp

Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Install model-specific dependencies:

# For transformers-based models
pip install "transformers>=4.31.0" accelerate bitsandbytes
 
# For GGML-quantized models
pip install llama-cpp-python --no-binary llama-cpp-python

Configure your server:

cp config/server.example.json config/server.json
# Edit config/server.json with your preferred settings

Model Selection and Configuration

The server configuration supports multiple model backends:

{
  "models": [
    {
      "name": "codellama-7b-instruct",
      "backend": "transformers",
      "model_path": "./models/codellama-7b-instruct",
      "quantization": "4bit",
      "context_window": 16384,
      "max_tokens": 2048,
      "temperature_range": [0.0, 2.0],
      "default_temperature": 0.2
    },
    {
      "name": "llama2-13b-code-gguf",
      "backend": "llama_cpp",
      "model_path": "./models/llama2-13b-code.Q4_K_M.gguf",
      "context_window": 8192,
      "max_tokens": 1024,
      "gpu_layers": 32
    }
  ]
}

Connecting Cursor to Your MCP Server

Cursor Configuration

Open Cursor and navigate to Settings (⚙️) > AI > Advanced Settings
Enable the "Custom MCP Servers" option
Add your server configuration:

{
  "servers": [
    {
      "name": "Local CodeLlama",
      "url": "ws://localhost:8765",
      "auth": {
        "type": "bearer",
        "token": "your_secret_token"
      },
      "default": true
    }
  ]
}

Save the configuration and restart Cursor

Connection Security

For production environments, implement secure WebSocket connections:

# server.py
import ssl
 
ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)
ssl_context.load_cert_chain('path/to/cert.pem', 'path/to/key.pem')
 
start_server = websockets.serve(
    ws_handler, 
    "0.0.0.0", 
    8765, 
    ssl=ssl_context,
    max_size=100_000_000  # Allow large context windows
)

Then update your Cursor configuration to use WSS:

{
  "servers": [
    {
      "name": "Secure CodeLlama",
      "url": "wss://your-server-address:8765",
      "auth": {
        "type": "bearer",
        "token": "your_secret_token"
      }
    }
  ]
}

Advanced Server Configuration

Request Prioritization

Implement a priority queue for handling multiple concurrent requests:

class RequestScheduler:
    def __init__(self):
        self.queue = PriorityQueue()
        self.active_requests = {}
        
    async def schedule_request(self, request, priority=0):
        task_id = str(uuid.uuid4())
        await self.queue.put((priority, task_id, request))
        return task_id
        
    async def process_queue(self, model_manager):
        while True:
            priority, task_id, request = await self.queue.get()
            # Process request with appropriate model
            result = await model_manager.generate(request)
            # Handle result...

Memory Management

Implement advanced memory management techniques to handle large models:

def load_model_with_memory_constraints(model_path, max_memory, device_map=None):
    # Calculate optimal device map based on available VRAM
    if not device_map:
        device_map = compute_device_map(max_memory)
    
    return AutoModelForCausalLM.from_pretrained(
        model_path,
        device_map=device_map,
        torch_dtype=torch.float16,
        low_cpu_mem_usage=True,
        offload_folder="offload_folder"
    )

Prompt Engineering Templates

Define system prompts for different coding tasks:

{
  "prompt_templates": {
    "code_completion": "You are an expert programmer. Complete the following code:\n\n{code}",
    "refactoring": "Refactor the following code to improve {aspect}:\n\n{code}",
    "documentation": "Write comprehensive documentation for the following code:\n\n{code}",
    "bug_fixing": "Fix the bugs in the following code:\n\n{code}\n\nError: {error}"
  }
}

Performance Optimization

Model Quantization

Implement model quantization for reduced memory usage:

# For transformers models
from transformers import BitsAndBytesConfig
 
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True
)
 
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    quantization_config=quantization_config,
    device_map="auto"
)

Response Caching

Implement an LRU cache for frequent requests:

from functools import lru_cache
 
@lru_cache(maxsize=1000)
async def cached_inference(model_name, prompt, temperature, max_tokens):
    # Perform actual inference
    return await run_inference(model_name, prompt, temperature, max_tokens)

Parallel Inference

For multi-GPU setups, implement parallel inference:

class ParallelInferenceManager:
    def __init__(self, model_paths, gpu_ids):
        self.models = {}
        for i, (model_path, gpu_id) in enumerate(zip(model_paths, gpu_ids)):
            self.models[f"gpu_{i}"] = self.load_model(model_path, gpu_id)
        self.current_gpu = 0
        
    def load_model(self, model_path, gpu_id):
        # Load model to specific GPU
        return Model(model_path, device=f"cuda:{gpu_id}")
        
    async def generate(self, request):
        # Round-robin GPU selection
        gpu_key = f"gpu_{self.current_gpu}"
        self.current_gpu = (self.current_gpu + 1) % len(self.models)
        
        # Run inference on selected GPU
        return await self.models[gpu_key].generate(request)

Troubleshooting Common Issues

Connection Problems

If Cursor fails to connect to your MCP server:

Verify the WebSocket server is running and accessible
Check firewall configurations to ensure port 8765 is open
Validate authentication token configuration matches between client and server
Inspect server logs for connection attempts and potential errors

# Test WebSocket connectivity
websocat ws://localhost:8765
# Should successfully connect if server is running properly

Out-of-Memory Errors

When encountering OOM issues:

Reduce batch size and increase gradient accumulation
Implement model sharding across multiple GPUs
Use lower precision (8-bit or 4-bit quantization)
Implement CPU offloading for specific layers

# Monitor GPU memory usage
def monitor_gpu_memory():
    total_memory = torch.cuda.get_device_properties(0).total_memory / 1024**3
    reserved = torch.cuda.memory_reserved(0) / 1024**3
    allocated = torch.cuda.memory_allocated(0) / 1024**3
    free = total_memory - reserved
    
    print(f"Total GPU memory: {total_memory:.2f} GB")
    print(f"Reserved: {reserved:.2f} GB")
    print(f"Allocated: {allocated:.2f} GB")
    print(f"Free: {free:.2f} GB")

Conclusion

Cursor MCP provides a powerful framework for integrating custom model servers with the Cursor editor. By implementing your own MCP server, you can leverage specialized models, custom prompt engineering, and advanced inference configurations to enhance your coding workflow. The flexibility of the protocol enables a wide range of use cases, from local model deployment for privacy-conscious environments to specialized inference servers optimized for specific programming languages or frameworks.

As the field of AI-assisted programming evolves, the ability to customize and control the model inference process will become increasingly valuable. By mastering Cursor MCP, you position yourself at the forefront of this technological integration, enabling truly personalized AI assistance for your development workflow.

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