DeepMyst MCP Server

Intelligent LLM Optimization & Routing for Claude Desktop and HTTP Clients

Overview

DeepMyst MCP Server creates a seamless bridge between DeepMyst and Claude Desktop or any client through the Model Context Protocol (MCP). This integration allows Claude and other clients to harness DeepMyst's powerful optimization and routing capabilities while maintaining your familiar workflows.

Related MCP server: ai-workers-mcp

Key Features

• Token Optimization - Reduce token usage by up to 75% while preserving response quality, directly lowering your API costs

• Smart Model Routing - Automatically select the optimal LLM for each specific query based on task requirements

• Combined Capabilities - Use both optimization and routing together for maximum efficiency

• Multiple Transport Support - Connect via STDIO (for Claude Desktop) or SSE (for HTTP clients)

• Client-provided API Keys - No need to store your DeepMyst API key on the server

How It Works

• Token Optimization: DeepMyst identifies redundancies in prompts, intelligently compresses content, preserves key information, and maintains contextual meaning—all while significantly reducing token usage without sacrificing quality.

• Smart Routing: The system analyzes each query's category, complexity level, and required capabilities. It evaluates available models based on performance benchmarks, token cost, response latency, and capability support through a weighted scoring system.

Installation

Prerequisites

• Python 3.8 or higher

• UV - Fast Python package installer

• Claude Desktop - Latest version (or another MCP client)

• DeepMyst API key (get one from platform.deepmyst.com)

Installation Steps

1. Clone or download the DeepMyst MCP Server code:

# Create a directory for the server
mkdir DeepMyst-MCP
cd DeepMyst-MCP

# Download the server code
# (Or copy the code from the provided deepmyst_mcp.py file)

2. Install dependencies with UV:

# Create and activate a virtual environment (optional but recommended)
uv venv
source .venv/bin/activate  # On macOS/Linux
# OR
.venv\Scripts\activate     # On Windows

# Install dependencies
uv pip install mcp openai aiohttp uvicorn starlette

Running the Server

Local Deployment

For Claude Desktop (STDIO Transport):

python deepmyst_mcp.py --stdio
# OR
uv run deepmyst_mcp.py --stdio

For HTTP Clients (SSE Transport):

python deepmyst_mcp.py --sse
# OR with custom host and port
python deepmyst_mcp.py --sse --host 127.0.0.1 --port 3000

The server now supports explicit command-line arguments:

• --stdio - Run with STDIO transport (for Claude Desktop)

• --sse - Run with SSE transport (for HTTP clients)

• --host HOST - Specify host address (default: 0.0.0.0)

• --port PORT - Specify port number (default: 8000)

• --debug - Enable debug logging

You can also use environment variables:

export HOST=127.0.0.1  # Change the host
export PORT=3000       # Change the port
python deepmyst_mcp.py --sse

Public Server

The DeepMyst MCP Server is publicly available at: https://mcp.deepmyst.com

Available endpoints:

• SSE endpoint: https://mcp.deepmyst.com/sse

• Message endpoint: https://mcp.deepmyst.com/messages

Configuring Claude Desktop

1. Open your Claude Desktop configuration file:

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

   • Windows: %APPDATA%\Claude\claude_desktop_config.json

2. Add the DeepMyst MCP server configuration:

{
  "mcpServers": {
    "deepmyst": {
      "command": "python",
      "args": [
        "/path/to/deepmyst_mcp.py",
        "--stdio"
      ]
    }
  }
}

3. Save the file and restart Claude Desktop

Tools and Capabilities

The DeepMyst MCP server provides several powerful tools:

1. Get Best Model for Query
   Analyzes your query and recommends the optimal model.

   Prompt: What's the best model for writing a poem about quantum physics?
   
   Parameters:
   - api_key: your-deepmyst-api-key

2. Optimized Completion
   Generates a response with token optimization to reduce costs.

   Prompt: Use optimized_completion to explain quantum entanglement, with these parameters:
   - api_key: your-deepmyst-api-key
   - model: gpt-4o
   - temperature: 0.7

3. Auto-Routed Completion
   Generates a response using the DeepMyst router to select the best model.

   Prompt: Use auto_routed_completion to generate a Python function with these parameters:
   - api_key: your-deepmyst-api-key
   - base_model: gpt-4o-mini

4. Smart Completion
   Combines routing and optimization - first determines the best model for your query, then optionally applies token optimization.

   Prompt: Use smart_completion to explain how blockchain works, with these parameters:
   - api_key: your-deepmyst-api-key
   - optimize: true

5. DeepMyst Completion
   The most flexible tool with all options configurable.

   Prompt: Use deepmyst_completion to summarize this article, with these parameters:
   - api_key: your-deepmyst-api-key
   - base_model: gpt-4o
   - optimize: true
   - auto_route: false

Advanced Use Cases

Collaborative Multi-LLM Problem Solving

DeepMyst enables complex problems to be broken down and distributed across multiple specialized LLMs:

• Parallel Processing: Divide complex tasks into subtasks that different LLMs can solve simultaneously

• Expert Ensembles: Assign different aspects of a problem to models with specific strengths (e.g., GPT-4o for creative writing, Claude for logical reasoning, Gemini for mathematical analysis)

• Consensus Building: Route the same question to multiple models and synthesize their responses for more reliable answers

• Chain-of-Thought Enhancement: Use one model to generate a reasoning path, then have another model verify or improve it

Example: For a complex business strategy analysis, DeepMyst could route market research to data-focused models, creative ideation to generative specialists, and risk assessment to models with better reasoning capabilities.

Handling Large Context Windows

Efficiently manage and process large documents or complex conversations:

• Context Chunking: Break large documents into manageable segments, route each to the appropriate model, then synthesize the results

• Progressive Summarization: Use models with smaller context windows to summarize sections, then feed those summaries to models with broader context capabilities

• Priority Filtering: Intelligently identify and preserve the most relevant context while compressing less important information

• Context Management: Maintain conversation history effectively without exceeding token limits by dynamically compressing older turns

Example: When analyzing a 500-page legal document, DeepMyst can chunk the document, route sections to specialized legal analysis models, and progressively build a comprehensive analysis without hitting context limits.

Intelligent Task Routing

Match tasks to the most suitable models based on their specific capabilities:

• Capability Matching: Automatically identify task requirements and match them with models that excel in those areas

• Cost Optimization: Route simple queries to efficient, lower-cost models while reserving premium models for complex tasks

• Latency Management: Select models based on response time requirements for time-sensitive applications

• Specialization Routing: Direct domain-specific questions to models with the best performance in those fields

Example: A financial analysis workflow could route data processing to fast, efficient models, numerical analysis to math-specialized models, and final report generation to models with better writing capabilities.

Web Integration with SSE Transport

With SSE transport support, you can integrate DeepMyst MCP into web applications:

• Remote Access: Access your DeepMyst MCP server from any client over HTTP

• Web Applications: Integrate with web frontends and backends

• Multiple Clients: Connect multiple clients simultaneously to the same server

• API Gateway: Use as a gateway for your own applications

Example: Build a web interface that connects to the DeepMyst MCP server, allowing users to interact with multiple LLMs through a unified UI.

Supported LLM Providers

DeepMyst supports multiple LLM providers including:

• OpenAI: GPT-4o, GPT-4o-mini, etc.

• Anthropic: Claude 3.7 Sonnet, Claude 3.5 Sonnet, etc.

• Google: Gemini 2.0 Flash, Gemini 1.5 Pro, etc.

• Groq: Llama 3.1, Mixtral, etc.

Security Considerations

API Key Handling

The server now requires clients to provide their DeepMyst API key with each tool call. This has several security implications:

• Advantages:

  • No API keys stored on the server

  • Each user can use their own API key

  • Server operator doesn't need access to API keys

• Considerations:

  • API keys will be visible in conversation history

  • Keys are passed with each tool call

  • May appear in logs if not properly configured

For enhanced security in production environments:

1. Always use HTTPS for server communication

2. Consider implementing a token-based authentication system

3. Implement key masking in logs

4. Use environment-specific security measures based on your deployment platform

Resources

• Sign Up: platform.deepmyst.com - Create a DeepMyst account and get your API key

• Try DeepMyst: ask.deepmyst.com - Interactive demo to experience DeepMyst's capabilities

• Documentation: docs.deepmyst.com - Comprehensive guides and API documentation

• Public MCP Server: mcp.deepmyst.com - Use the public DeepMyst MCP server

Troubleshooting

Common Issues

Claude doesn't show the hammer icon:

• Make sure Claude Desktop is up to date

• Check your configuration file for syntax errors

• Restart Claude Desktop

API Key errors:

• Verify your DeepMyst API key is correct

• Ensure you're providing the API key with each tool call

• Check for typos in the API key parameter name

Server connection issues:

• Check that all dependencies are installed

• Verify the path to deepmyst_mcp.py is correct

• Look for error messages in the terminal running the server

SSE transport issues:

• Check that the port is not in use by another application

• Verify network connectivity between client and server

• Look for firewall or network restrictions that might block the connection

MCP Inspector connectivity:

• Use proper format for connecting: npx @modelcontextprotocol/inspector dev http://localhost:8000

• Avoid using --env parameter which may cause parsing issues

Logs

Check the DeepMyst MCP server logs for more detailed troubleshooting information. Logs are written to deepmyst.log in the same directory as the server script.

You can enable debug logging with the --debug flag:

python deepmyst_mcp.py --stdio --debug

Health Check

For SSE deployments, you can check server health by accessing the deepmyst://health resource:

Prompt: Check the health status of the DeepMyst MCP server.

Using the Public Server

To connect to the public DeepMyst MCP server:

For Web Clients

Connect to the public server using the SSE endpoints:

const mcpClient = new McpClient({
  sseEndpoint: "https://mcp.deepmyst.com/sse",
  messageEndpoint: "https://mcp.deepmyst.com/messages"
});

For Command Line Clients

Use the MCP CLI with the SSE transport:

mcp connect sse --url https://mcp.deepmyst.com

Using MCP Inspector

To connect the MCP Inspector to the public server:

npx @modelcontextprotocol/inspector dev https://mcp.deepmyst.com

DeepMyst MCP Server is licensed under the MIT License