local-llm-mcp-server

georgepok/local-llm-mcp-server

3.2

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A Model Context Protocol (MCP) server that bridges local LLMs running in LM Studio with Claude Desktop and other MCP clients, ensuring privacy by processing AI tasks locally.

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Local LLM MCP Server

A Model Context Protocol (MCP) server that bridges local LLMs running in LM Studio with Claude Desktop and other MCP clients. Keep your sensitive data private by running AI tasks locally while seamlessly integrating with cloud-based AI assistants.

License: MIT Node.js Version TypeScript

🌟 Features

🔒 Privacy-First Design

  • Local Processing: All sensitive data stays on your machine
  • No Cloud Exposure: Private analysis, code review, and content processing happens locally
  • Privacy Levels: Configurable privacy protection (strict, moderate, minimal)
  • No Telemetry: Zero usage tracking or data collection

🤖 Dynamic Multi-Model Support

  • Auto-Discovery: Automatically detects all models loaded in LM Studio
  • Flexible Selection: Use different models for different tasks
  • Runtime Switching: Change default models during your session
  • Per-Request Override: Specify model for individual requests
  • Smart Initialization: First available model auto-selected as default

🛠️ Comprehensive Tool Suite

Local Reasoning - General-purpose AI tasks with complete privacy

  • Complex problem solving and multi-step reasoning
  • Question answering and task planning
  • Context-aware responses

Private Analysis - 7 analysis types for sensitive content

  • Sentiment Analysis (domain-aware)
  • Entity Extraction (people, orgs, locations, domain-specific)
  • Content Classification
  • Summarization with key points
  • Privacy Scanning (PII, GDPR compliance)
  • Security Auditing (vulnerabilities, misconfigurations)

Secure Rewriting - Transform text while maintaining privacy

  • Style adaptation (formal, casual, professional)
  • Sensitive information removal
  • Privacy-preserving transformations

Code Analysis - Local code review and security

  • Security vulnerability detection
  • Code quality assessment
  • Bug detection and optimization suggestions

Template Completion - Intelligent form and document filling

🎯 Domain-Specific Intelligence

Specialized analysis for:

  • Medical: Healthcare context, HIPAA compliance, clinical terminology
  • Legal: Legal terminology, regulatory compliance, confidentiality
  • Financial: Financial regulations, market analysis, data protection
  • Technical: Software development, engineering contexts
  • Academic: Scholarly research, methodology, citations

🚀 Quick Start

Prerequisites

  1. Node.js 18+

    node --version  # Should be >= 18.0.0

  2. LM Studio

    • Download from lmstudio.ai
    • Load at least one model (e.g., Llama 3.2, Qwen, Mistral)
    • Start the local server (Server tab → Start Server)
    • Default URL: http://localhost:1234

Installation

git clone https://github.com/yourusername/local-llm-mcp-server.git
cd local-llm-mcp-server
npm install
npm run build

Configure Claude Desktop

Edit your Claude Desktop config file:

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

Add this configuration:

{
  "mcpServers": {
    "local-llm": {
      "command": "node",
      "args": ["/absolute/path/to/local-llm-mcp-server/dist/index.js"]
    }
  }
}

Important: Use the absolute path to your installation.

Start Using

  1. Restart Claude Desktop - The server starts automatically
  2. Discover Models - Read resource local://models to see available models
  3. Try It - Ask Claude to use the local_reasoning tool with a simple prompt

The server automatically:

  • Discovers all models loaded in LM Studio
  • Sets the first model as default
  • Provides full capability documentation via local://capabilities

Convenience Scripts

For easier server management, use the included scripts:

# Start in local mode (stdio - for Claude Desktop)
npm run start:local

# Start in remote mode (HTTP - for network access)
npm run start:remote

# Start in secure mode (HTTPS - encrypted network access)
npm run start:https

# Start in dual mode (stdio + HTTP for both local and remote)
npm run start:dual

# Generate SSL certificates for HTTPS
npm run generate:certs

# Stop all running servers
npm run stop

See for detailed usage.

🌐 Remote Network Access

Access the server from other devices on your home network or connect Claude Desktop remotely!

Connect Claude Desktop Remotely

Quick Start (3 steps):

# 1. Start HTTPS server
npm run start:https

# 2. Add to claude_desktop_config.json:
{
  "mcpServers": {
    "local-llm-remote": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://localhost:3010/sse"],
      "env": {"NODE_TLS_REJECT_UNAUTHORIZED": "0"}
    }
  }
}

# 3. Restart Claude Desktop

📖 Complete Guide:

Remote Access Methods

Method 1: Claude Desktop Custom Connector UI (Production)

  • For Claude Pro/Max/Team/Enterprise users
  • Requires valid SSL certificate (not self-signed)
  • Simple UI-based setup in Settings > Connectors
  • Guide:

Method 2: mcp-remote Proxy (Development/Testing)

  • Works with self-signed certificates
  • Supports localhost and local networks
  • JSON configuration file
  • Examples:

Network Access from Any Client

# Start in HTTP mode for network access
npm run start:remote

# Access from any device on your network
curl http://192.168.1.100:3000/health

Available endpoints:

  • / - Server information
  • /health - Health check
  • /mcp - MCP Streamable HTTP endpoint (GET/POST/DELETE)

See for complete guide including:

  • Firewall configuration
  • Client examples (JavaScript, Python, cURL)
  • Troubleshooting
  • Multiple device scenarios

Transport Modes:

  • Local Mode (stdio): For Claude Desktop integration
  • HTTP Mode: Unencrypted network access
  • HTTPS Mode: Encrypted network access with SSL/TLS
  • Dual Mode: Run stdio + HTTP/HTTPS simultaneously!

See for secure setup and dual mode usage.

Specification Compliance

✅ MCP Streamable HTTP Transport (Protocol 2025-03-26)

Our implementation uses the latest MCP Streamable HTTP transport:

  • Protocol version: 2025-03-26
  • Full JSON-RPC 2.0 compliance
  • Session management via headers
  • SSE streaming for responses
  • Stateful mode with session IDs
# Run Streamable HTTP test
npm run test:streamable

🔄 Migration Note: SSE transport (2024-11-05) has been replaced with Streamable HTTP per MCP specification. See for migration guide.

📋 Available Tools

Core Tools

local_reasoning

Use the local LLM for specialized reasoning tasks while keeping data private.

// Example usage in Claude
await tools.local_reasoning({
  prompt: "Analyze the logical flow of this argument...",
  system_prompt: "You are a critical thinking expert",
  model_params: {
    temperature: 0.7,
    max_tokens: 1500
  }
});
private_analysis

Analyze sensitive content locally without cloud exposure.

await tools.private_analysis({
  content: "Confidential business document...",
  analysis_type: "sentiment", // or "entities", "classification", etc.
  domain: "financial"
});
secure_rewrite

Rewrite or transform text locally for privacy.

await tools.secure_rewrite({
  content: "Original text with sensitive info...",
  style: "professional",
  privacy_level: "strict"
});
code_analysis

Analyze code locally for security, quality, or documentation.

await tools.code_analysis({
  code: "function processUserData(input) { ... }",
  language: "javascript",
  analysis_focus: "security"
});
template_completion

Complete templates or forms using the local LLM.

await tools.template_completion({
  template: "Dear [NAME], Thank you for [ACTION]...",
  context: "Customer submitted a support ticket about billing",
  format: "email"
});

📚 Resources

Available Resources

  • local://models - List of available models in LM Studio
  • local://status - Current status of the local LLM server
  • local://config - Server configuration and capabilities

Example Resource Usage

// Get available models
const models = await resources.read("local://models");

// Check server status
const status = await resources.read("local://status");

// View configuration
const config = await resources.read("local://config");

🎯 Prompt Templates

Using Pre-built Templates

// Privacy analysis template
await prompts.get("privacy-analysis", {
  content: "Document to analyze...",
  regulation: "GDPR"
});

// Code security review template
await prompts.get("code-security-review", {
  code: "const user = req.body.user;",
  language: "javascript",
  security_focus: "injection vulnerabilities"
});

// Meeting summary template
await prompts.get("meeting-summary", {
  meeting_content: "Meeting transcript...",
  participants: "Alice, Bob, Charlie",
  focus_areas: "action items, decisions"
});

Available Templates

  • Privacy & Security: privacy-analysis, secure-rewrite
  • Code Analysis: code-security-review, code-optimization
  • Business: meeting-summary, email-draft, risk-assessment
  • Research: research-synthesis, literature-review
  • Content: content-adaptation, technical-documentation

⚙️ Configuration

Model Configuration

Configure different models for different capabilities:

{
  "models": {
    "reasoning": {
      "name": "Reasoning Model",
      "capabilities": ["reasoning", "analysis", "problem-solving"],
      "defaultParams": {
        "temperature": 0.7,
        "max_tokens": 2000
      }
    },
    "privacy": {
      "name": "Privacy Model",
      "capabilities": ["privacy", "anonymization", "security"],
      "defaultParams": {
        "temperature": 0.2,
        "max_tokens": 1500
      }
    }
  }
}

Privacy Settings

{
  "privacy": {
    "defaultLevel": "moderate",
    "enableLogging": false,
    "logRetentionDays": 7
  }
}

Performance Tuning

{
  "performance": {
    "cacheEnabled": true,
    "cacheTTL": 3600,
    "maxConcurrentRequests": 5,
    "requestTimeout": 60000
  }
}

🔒 Privacy Levels

Strict

  • Never expose personal names, addresses, phone numbers, emails
  • Generalize all specific locations and dates
  • Remove all identifying information
  • Use placeholders for sensitive data

Moderate

  • Protect personal identifiable information
  • Generalize specific details when appropriate
  • Maintain readability while ensuring privacy
  • Remove sensitive financial or health data

Minimal

  • Protect obvious sensitive information (SSNs, credit cards)
  • Remove personal contact information
  • Maintain the natural flow of the text

🛠️ Development

Project Structure

src/
├── index.ts              # Main MCP server
├── types.ts              # TypeScript type definitions
├── lm-studio-client.ts   # LM Studio API client
├── privacy-tools.ts      # Privacy-preserving functions
├── analysis-tools.ts     # Content analysis tools
├── prompt-templates.ts   # Pre-built prompt templates
└── config.ts            # Configuration management

Building

# TypeScript compilation
npm run build

# Type checking
npm run typecheck

# Development with auto-reload
npm run dev

Adding Custom Tools

  1. Define the tool in index.ts:
this.server.setRequestHandler(CallToolRequestSchema, async (request) => {
  // ... existing tools
  case 'my-custom-tool':
    result = await this.handleCustomTool(args);
    break;
});
  1. Implement the handler:
private async handleCustomTool(args: any): Promise<MCPResponse> {
  // Your custom logic here
  const response = await this.lmStudio.generateResponse(
    args.prompt,
    args.system_prompt
  );

  return {
    content: [{ type: 'text', text: response }]
  };
}

🔍 Troubleshooting

Common Issues

LM Studio Connection Issues

  • Ensure LM Studio is running and the server is started
  • Check that the base URL matches your LM Studio configuration
  • Verify the model is loaded and available

Performance Issues

  • Adjust the maxConcurrentRequests setting
  • Increase requestTimeout for complex requests
  • Consider using a more powerful local model

Privacy Concerns

  • Review and adjust privacy level settings
  • Enable strict privacy mode for sensitive data
  • Disable logging if handling confidential information

Debug Mode

Set environment variable for verbose logging:

DEBUG=mcp:* npm start

📄 License

MIT License - see LICENSE file for details.

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests if applicable
  5. Submit a pull request

📞 Support

  • Create an issue for bug reports
  • Start a discussion for feature requests
  • Check the documentation for common questions

Note: This server is designed to work with local LLMs for privacy-sensitive tasks. Always review the privacy settings and ensure they meet your requirements before processing confidential data.