Ultra MCP

All Models. One Interface. Zero Friction.

🚀 Ultra MCP - A Model Context Protocol server that exposes OpenAI, Gemini, Azure OpenAI, and xAI Grok AI models through a single MCP interface for use with Claude Code and Cursor.

Stop wasting time having meetings with human. Now it's time to ask AI models do this.

Inspiration

This project is inspired by:

Agent2Agent (A2A) by Google - Thank you Google for pioneering agent-to-agent communication protocols
Zen MCP - The AI orchestration server that enables Claude to collaborate with multiple AI models

Why Ultra MCP?

While inspired by zen-mcp-server, Ultra MCP offers several key advantages:

🚀 Easier to Use

No cloning required - Just run npx ultra-mcp to get started
NPM package - Install globally with npm install -g ultra-mcp
Interactive setup - Guided configuration with npx ultra-mcp config
Zero friction - From zero to AI-powered coding in under a minute

📊 Built-in Usage Analytics

Local SQLite database - All usage data stored locally using libSQL
Automatic tracking - Every LLM request is tracked with token counts and costs
Usage statistics - View your AI usage with npx ultra-mcp db:stats
Privacy first - Your data never leaves your machine

🌐 Modern Web Dashboard

Beautiful UI - React dashboard with Tailwind CSS
Real-time stats - View usage trends, costs by provider, and model distribution
Easy access - Just run npx ultra-mcp dashboard
Configuration UI - Manage API keys and model priorities from the web

🔧 Additional Benefits

Simplified tools - Maximum 4 parameters per tool (vs zen's 10-15)
Smart defaults - Optimal model selection out of the box
TypeScript first - Full type safety and better developer experience
Regular updates - Active development with new features weekly

Features

🤖 Multi-Model Support: Integrate OpenAI (GPT-5), Google Gemini (2.5 Pro), Azure OpenAI, and xAI Grok models
🔌 MCP Protocol: Standard Model Context Protocol interface
🧠 Deep Reasoning Tools: Access GPT-5 for complex problem-solving
🔍 Investigation & Research: Built-in tools for thorough investigation and research
🌐 Google Search Integration: Gemini 2.5 Pro with real-time web search
⚡ Real-time Streaming: Live model responses via Vercel AI SDK
🔧 Zero Config: Interactive setup with smart defaults
🔑 Secure Configuration: Local API key storage with conf library
🧪 TypeScript: Full type safety and modern development experience

Quick Start

Installation

# Install globally via npm
npm install -g ultra-mcp

# Or run directly with npx
npx -y ultra-mcp config

Configuration

Set up your API keys interactively:

npx -y ultra-mcp config

This will:

Show current configuration status
Present a provider-first menu to select which AI provider to configure
Guide you through setting API keys, base URLs, and preferred models
Store configuration securely on your system
Auto-load settings when the server starts

New in v0.5.10:

🎯 Provider-first configuration - Select specific provider to configure
🤖 OpenAI-Compatible support - Configure Ollama (local) or OpenRouter (400+ models)
📋 Model selection - Choose your preferred model from categorized lists

Running the Server

# Run the MCP server
npx -y ultra-mcp

# Or after building locally
bun run build
node dist/cli.js

CLI Commands

Ultra MCP provides several powerful commands:

`config` - Interactive Configuration

npx -y ultra-mcp config

Configure API keys interactively with a user-friendly menu system.

`dashboard` - Web Dashboard

npx -y ultra-mcp dashboard

# Custom port
npx -y ultra-mcp dashboard --port 4000

# Development mode
npx -y ultra-mcp dashboard --dev

Launch the web dashboard to view usage statistics, manage configurations, and monitor AI costs.

`install` - Install for Claude Code

npx -y ultra-mcp install

Automatically install Ultra MCP as an MCP server for Claude Code.

`doctor` - Health Check

npx -y ultra-mcp doctor

# Test connections to providers
npx -y ultra-mcp doctor --test

Check installation health and test API connections.

`chat` - Interactive Chat

npx -y ultra-mcp chat

# Specify model and provider
npx -y ultra-mcp chat -m gpt-5 -p openai
npx -y ultra-mcp chat -m grok-4 -p grok

Chat interactively with AI models from the command line.

Database Commands

`db:show` - Show Database Info

npx -y ultra-mcp db:show

Display database file location and basic statistics.

`db:stats` - Usage Statistics

npx -y ultra-mcp db:stats

Show detailed usage statistics for the last 30 days including costs by provider.

`db:view` - Database Viewer

npx -y ultra-mcp db:view

Launch Drizzle Studio to explore the usage database interactively.

Integration with Claude Code

Automatic Installation (Recommended)

# Install Ultra MCP for Claude Code
npx -y ultra-mcp install

This command will:

Detect Claude Code installation
Add Ultra MCP as an MCP server
Configure for user or project scope
Verify API key configuration

Manual Installation

Add to your Claude Code settings:

{
  "mcpServers": {
    "ultra-mcp": {
      "command": "npx",
      "args": ["-y", "ultra-mcp@latest"]
    }
  }
}

Integration with Cursor

First configure your API keys:

npx -y ultra-mcp config

Then add to your Cursor MCP settings:

{
  "mcpServers": {
    "ultra-mcp": {
      "command": "npx",
      "args": ["-y", "ultra-mcp@latest"]
    }
  }
}

Ultra MCP will automatically use the API keys you configured with the config command.

MCP Tools

Ultra MCP provides powerful AI tools accessible through Claude Code and Cursor:

🧠 Deep Reasoning (`deep-reasoning`)

Leverage advanced AI models for complex problem-solving and analysis.

Default: GPT-5 for OpenAI/Azure, Gemini 2.5 Pro with Google Search, Grok-4 for xAI
Use Cases: Complex algorithms, architectural decisions, deep analysis

🔍 Investigate (`investigate`)

Thoroughly investigate topics with configurable depth levels.

Depth Levels: shallow, medium, deep
Google Search: Enabled by default for Gemini
Use Cases: Research topics, explore concepts, gather insights

📚 Research (`research`)

Conduct comprehensive research with multiple output formats.

Output Formats: summary, detailed, academic
Use Cases: Literature reviews, technology comparisons, documentation

📋 List Models (`list-ai-models`)

View all available AI models and their configuration status.

Example Usage

// In Claude Code or Cursor with MCP
await use_mcp_tool('ultra-mcp', 'deep-reasoning', {
  provider: 'openai',
  prompt: 'Design a distributed caching system for microservices',
  reasoningEffort: 'high',
});

Development

# Clone the repository
git clone https://github.com/RealMikeChong/ultra-mcp
cd ultra-mcp

# Install dependencies
bun install

# Build TypeScript
bun run build

# Run tests
bun run test

# Development mode with watch
bun run dev

# Test with MCP Inspector
npx @modelcontextprotocol/inspector node dist/cli.js

Architecture

Ultra MCP acts as a bridge between multiple AI model providers and MCP clients:

MCP Protocol Layer: Implements Model Context Protocol for Claude Code/Cursor communication
Model Providers: Integrates OpenAI, Google (Gemini), Azure OpenAI, and xAI Grok via Vercel AI SDK
Unified Interface: Single MCP interface to access multiple AI models
Configuration Management: Secure local storage with schema validation

Key Components

src/cli.ts - CLI entry point with commander
src/server.ts - MCP server implementation
src/config/ - Configuration management with schema validation
src/handlers/ - MCP protocol handlers
src/providers/ - Model provider implementations
src/utils/ - Shared utilities for streaming and error handling

Configuration Storage

Ultra MCP stores configuration in your system's default config directory:

macOS: ~/Library/Preferences/ultra-mcp-nodejs/
Linux: ~/.config/ultra-mcp/
Windows: %APPDATA%\ultra-mcp-nodejs\

Environment Variables

You can also set API keys and base URLs via environment variables:

OPENAI_API_KEY / OPENAI_BASE_URL
GOOGLE_API_KEY / GOOGLE_BASE_URL
AZURE_API_KEY / AZURE_BASE_URL (base URL required for Azure)
XAI_API_KEY / XAI_BASE_URL

Note: Configuration file takes precedence over environment variables.

Vector Embeddings Configuration

Ultra MCP supports vector embeddings for semantic code search. By default, it uses text-embedding-3-small for cost efficiency (6.5x cheaper than the large model).

Embedding Model Configuration

You can customize the embedding models in your configuration:

{
  "vectorConfig": {
    "embeddingModel": {
      "openai": "text-embedding-3-small",  // or "text-embedding-3-large"
      "azure": "text-embedding-3-small",    // or "text-embedding-3-large"
      "gemini": "text-embedding-004"
    }
  }
}

Model Comparison

Model	Cost	Dimensions	MTEB Score	Best For
text-embedding-3-small	$0.02/1M tokens	1536	62.3%	Cost-effective code search
text-embedding-3-large	$0.13/1M tokens	3072	64.6%	Maximum accuracy

Migration Notes

Existing Databases: If you have an existing vector database created with text-embedding-3-large, it will continue to work but won't be compatible with new embeddings from text-embedding-3-small. Consider re-indexing if you want to use the smaller model.
Backward Compatibility: You can always override the model by configuring embeddingModel in your vector config.

Roadmap

Phase 1: Zero Config Setup

Interactive mode for seamless first-time setup
Auto-detection of available API keys
Smart defaults and configuration recommendations
One-command installation and setup

Phase 2: Integration Helpers

Helper commands to integrate Ultra MCP into Claude Code
Cursor IDE integration utilities
Auto-generation of MCP server configuration files
Integration validation and troubleshooting tools

Phase 3: Cost Dashboard & Analytics

Web UI dashboard using React, shadcn/ui, and Tremor
SQLite database for usage tracking via Drizzle ORM
Real-time cost monitoring and budget alerts
Usage analytics and model performance insights
Export capabilities for billing and reporting

Phase 4: Workflow Optimization

Use Ultra MCP to 100x your current LLM coding workflows
Advanced prompt templates and automation
Multi-model orchestration and fallback strategies
Workflow optimization recommendations
Performance monitoring and optimization tools

Contributing

Fork the repository
Create a feature branch: git checkout -b feature-name
Make your changes and add tests
Run tests: npm test
Commit changes: git commit -m "Add feature"
Push to the branch: git push origin feature-name
Submit a pull request

Testing

# Run all tests
bun run test

# Run tests with UI
bun run test:ui

# Run tests with coverage
bun run test:coverage

License

MIT License - see LICENSE file for details.

Acknowledgments

Google for the Agent2Agent (A2A) Protocol inspiring agent interoperability
BeehiveInnovations for Zen MCP demonstrating AI model orchestration
Anthropic for the Model Context Protocol
Vercel for the excellent AI SDK

About the Author

👋 Mike Chong - Building tools to amplify human potential through AI.

As one of the earliest users of GitHub Copilot (personally invited by Nat Friedman, former GitHub CEO), I've witnessed firsthand how AI-assisted development can transform the way we build software. My journey as a former engineer on Outlook iOS/Android taught me the importance of creating tools that genuinely improve people's daily lives.

Ultra MCP represents my vision of democratizing access to the best AI models, making cutting-edge AI capabilities accessible to every developer through a unified, simple interface. I believe that by removing barriers between developers and AI models, we can accelerate innovation and create a better world for everyone.

"The future belongs to those who can seamlessly orchestrate human creativity with AI capabilities."

Why Ultra MCP is Different from Zen MCP Server

While both projects aim to enhance AI development workflows, Ultra MCP brings unique advantages:

Written in TypeScript - Full type safety, excellent IDE support, and a more maintainable codebase
Vector Search Support - Built-in semantic code search using vector embeddings
- Index your entire codebase with npx ultra-mcp index
- Search with natural language queries: npx ultra-mcp search "authentication logic"
- Powered by OpenAI, Azure OpenAI, and Google Gemini embeddings
- Local SQLite storage with libSQL vector extension for efficient similarity search
- Smart chunking and overlap for optimal search results
Built-in Dashboard & Usage Tracking - Comprehensive analytics and cost monitoring
- Web dashboard with live metrics and real-time statistics
- Automatic tracking of all LLM requests with token counts
- Continuously updated pricing via LiteLLM - Accurate cost calculations
- Tiered pricing support (e.g., Gemini's long-context pricing tiers)
- SQLite database powered by libSQL for local-first privacy
Advanced Pricing System - Real-time cost management
- Fetches latest pricing from LiteLLM's GitHub repository
- File-based caching with 1-hour TTL to minimize network calls
- CLI commands: npx ultra-mcp pricing show gpt-4o
- Accurate cost tracking for hundreds of models across all providers
- Automatic fallback to cached data when offline

Unlike many MCP implementations, Ultra MCP includes built-in vector search and a pricing-aware dashboard out of the box. These features make Ultra MCP particularly suited for developers who want robust tooling with built-in cost visibility and intelligent code search capabilities for responsible AI usage.

Links

Install Server

HTTP connection URL

security – no known vulnerabilities

license - permissive license

quality - confirmed to work

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Tools

View all tools

A Model Context Protocol server that exposes OpenAI and Gemini AI models through a single interface, allowing tools like Claude Code and Cursor to access multiple AI providers with built-in usage analytics.

Related MCP Servers

Gemini MCP Server
lbds137
-
security
A
license
-
quality
A Model Context Protocol server that enables Claude to collaborate with Google's Gemini AI models, providing tools for question answering, code review, brainstorming, test generation, and explanations.
Last updated -
Python
MIT License
Gemini MCP Server
D-Diaa
-
security
F
license
-
quality
A Model Context Protocol server that enables Claude to interact with Google's Gemini AI models, allowing users to ask Gemini questions directly from Claude.
Last updated -
1
Python
Zen MCP Server
BeehiveInnovations
-
security
F
license
-
quality
A Model Context Protocol server that gives Claude access to multiple AI models (Gemini, OpenAI, OpenRouter) for enhanced code analysis, problem-solving, and collaborative development through AI orchestration with conversations that continue across tasks.
Last updated -
5,958
Python
MCP AI Bridge
fakoli
A
security
A
license
A
quality
A secure Model Context Protocol server that enables Claude Code to connect with OpenAI and Google Gemini models, allowing users to query multiple AI providers through a standardized interface.
Last updated -
3
2
JavaScript
MIT License

View all related MCP servers