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marcosfpina

MLX MCP Server

by marcosfpina
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MLX MCP Server 🚀

Model Context Protocol server for ML Offload System

Unified interface for LLM inference management via Model Context Protocol (MCP). Control your ML models directly from Claude Desktop, VSCode, Zed Editor, or Amazon Bedrock.

Features

Core Functionality

  • 🧠 Model Registry Access - List, search, and inspect ML models

  • 🎮 Backend Control - Load/unload/switch models across backends (Ollama, llama.cpp, vLLM, TGI)

  • 📊 VRAM Monitoring - Real-time GPU memory status

  • ⚡ Hot-Reload - Switch models without restarting services

  • 🔧 Auto-Discovery - Trigger model scans to update registry

  • 🏥 Health Checks - Monitor ML Offload API status

💰 Token Economy (NEW!)

  • Smart Caching - Per-tool TTL (5min for models, 10s for VRAM)

  • Auto-Summarization - Concise responses by default (~80-90% token savings)

  • Rate Limiting - Prevents API abuse and excessive costs

  • Cache Statistics - Monitor hit rate and savings

  • Verbose Mode - Full JSON when needed (verbose: true)

Cost Savings: ~80% reduction in API costs (Amazon/Anthropic billing)

Related MCP server: Kernel MCP Server

Prerequisites

  1. ML Offload API running on http://localhost:9000 (See /etc/nixos/modules/ml/offload for setup)

  2. Node.js 18+

    node --version  # Should be 18.0.0 or higher

Installation

# Clone or navigate to this directory
cd ~/dev/mlx-mcp

# Install dependencies
npm install

# Build TypeScript
npm run build

Configuration

Environment Variables

# Optional: Change ML Offload API URL (default: http://localhost:9000)
export ML_OFFLOAD_API_URL="http://localhost:9000"

Usage

1. Claude Desktop (Recommended)

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "mlx": {
      "command": "node",
      "args": ["/home/kernelcore/dev/mlx-mcp/dist/index.js"],
      "env": {
        "ML_OFFLOAD_API_URL": "http://localhost:9000"
      }
    }
  }
}

Restart Claude Desktop and you'll see MLX tools available!

2. VSCode / VSCodium (via Continue extension)

  1. Install Continue extension

  2. Add to .continue/config.json:

{
  "experimental": {
    "modelContextProtocol": true
  },
  "mcpServers": {
    "mlx": {
      "command": "node",
      "args": ["/home/kernelcore/dev/mlx-mcp/dist/index.js"]
    }
  }
}

  1. Reload VSCode and access via Continue chat

3. Zed Editor

Add to Zed settings (~/.config/zed/settings.json):

{
  "context_servers": {
    "mlx-mcp": {
      "command": "node",
      "args": ["/home/kernelcore/dev/mlx-mcp/dist/index.js"]
    }
  }
}

4. Amazon Bedrock

Use with Bedrock Agent runtime via stdio:

import subprocess
import json

# Start MCP server
process = subprocess.Popen(
    ["node", "/home/kernelcore/dev/mlx-mcp/dist/index.js"],
    stdin=subprocess.PIPE,
    stdout=subprocess.PIPE,
    stderr=subprocess.PIPE
)

# Send MCP request
request = {
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
        "name": "list_models",
        "arguments": {}
    }
}

process.stdin.write((json.dumps(request) + "\n").encode())
process.stdin.flush()

# Read response
response = process.stdout.readline()
print(json.loads(response))

Available Tools

📋 Model Management

  • list_models - List all models in registry

    • Filters: format, backend, limit

    • Example: "List all GGUF models compatible with llamacpp"

  • get_model_info - Get detailed model information

    • Args: model_id

    • Example: "Show details for model ID 5"

  • trigger_model_scan - Scan /var/lib/ml-models for new models

    • No args required

    • Example: "Scan for new models"

⚙️ Backend Operations

  • list_backends - Show all available backends

    • No args required

    • Example: "Show available backends"

  • load_model - Load model on backend

    • Args: model_id, backend, priority (optional), gpu_layers (optional)

    • Example: "Load model 3 on llamacpp with 24 GPU layers"

  • unload_model - Unload model from backend

    • Args: backend

    • Example: "Unload model from ollama"

  • switch_model - Hot-switch model on backend

    • Args: backend, model_id, gpu_layers (optional)

    • Example: "Switch llamacpp to model 7"

📊 Monitoring

  • get_vram_status - Real-time GPU VRAM status

    • No args required

    • Example: "Show VRAM usage"

  • get_status - Complete system status

    • Shows VRAM, backends, loaded models

    • Example: "Show system status"

  • health_check - Check ML Offload API health

    • No args required

    • Example: "Is the ML API healthy?"

Example Prompts (for Claude/AI Assistants)

"List all GGUF models smaller than 5GB"
→ Calls: list_models with format filter

"What's the VRAM usage right now?"
→ Calls: get_vram_status

"Load mistral-7b-q4 on llamacpp with 28 GPU layers"
→ Calls: load_model with specific params

"Show me model 12's details"
→ Calls: get_model_info with model_id=12

"Scan for new models I just added"
→ Calls: trigger_model_scan

"Switch ollama to the new qwen model (ID 18)"
→ Calls: switch_model with backend=ollama, model_id=18

Development

# Watch mode (auto-rebuild on changes)
npm run dev

# Build
npm run build

# Run
npm start

Troubleshooting

"Connection refused" error

Problem: MCP server can't reach ML Offload API

Solution:

# Check if ML Offload API is running
curl http://localhost:9000/health

# If not, enable it in NixOS config:
# /etc/nixos/hosts/kernelcore/configuration.nix
kernelcore.ml.offload.enable = true;
kernelcore.ml.offload.api.enable = true;

# Rebuild NixOS
sudo nixos-rebuild switch

Tools not appearing in Claude Desktop

Problem: MCP tools not showing up

Solution:

  1. Check config file path is correct

  2. Verify dist/index.js exists (run npm run build)

  3. Restart Claude Desktop completely

  4. Check logs in Console.app (macOS) or journalctl (Linux)

"Permission denied" error

Problem: Can't execute server

Solution:

chmod +x /home/kernelcore/dev/mlx-mcp/dist/index.js

Architecture

┌─────────────────────────────────────────────────────────────┐
│                    AI Assistant (Claude/VSCode/Zed)         │
└────────────────────────┬────────────────────────────────────┘
                         │ MCP Protocol (stdio)
                         │
┌────────────────────────▼────────────────────────────────────┐
│                     MLX MCP Server (TypeScript)             │
│  - Tool handlers                                            │
│  - Request/Response formatting                              │
│  - Error handling                                           │
└────────────────────────┬────────────────────────────────────┘
                         │ HTTP REST API
                         │
┌────────────────────────▼────────────────────────────────────┐
│              ML Offload Manager API (Rust + Axum)           │
│  - Model Registry (SQLite)                                  │
│  - VRAM Intelligence (nvidia-smi)                           │
│  - Backend Orchestration                                    │
└────────────────────────┬────────────────────────────────────┘
                         │
        ┌────────────────┼────────────────┐
        │                │                │
        ▼                ▼                ▼
    ┌────────┐      ┌─────────┐     ┌──────┐
    │ Ollama │      │llama.cpp│     │ vLLM │
    └────────┘      └─────────┘     └──────┘

License

MIT - See LICENSE file

Support

Made with ❤️ by kernelcore

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