Chat MCP Server

What does “Chat MCP” MCP Server do?

Chat MCP is a desktop chat application that leverages the Model Context Protocol (MCP) to interface with various Large Language Models (LLMs). Built with Electron for cross-platform compatibility, Chat MCP allows users to connect with and manage multiple LLM backends, providing a unified interface to test, interact with, and configure different AI models. Its minimalistic codebase is designed to help developers and researchers understand MCP’s core principles, rapidly prototype with different servers, and streamline workflows involving LLMs. Key features include dynamic LLM configuration, multi-client management, and easy adaptation for both desktop and web environments.

List of Prompts

No prompt templates are mentioned in the available documentation or repository files.

List of Resources

No explicit MCP resources are documented in the repository or configuration examples.

List of Tools

No specific tools are listed or described within the repository or server.py (the repo does not contain a server.py file or equivalent tooling definitions).

Use Cases of this MCP Server

• Unified LLM Testing Platform
  Chat MCP enables developers to quickly configure and test multiple LLM providers and models within a single interface, streamlining the evaluation process.

• Cross-Platform AI Chat Application
  By supporting Linux, macOS, and Windows, Chat MCP can be used as a desktop chat client for interacting with AI models on any major operating system.

• Development and Debugging of MCP Integrations
  With its clean codebase, developers can use Chat MCP as a reference or starting point for building or debugging their own MCP-compatible applications.

• Educational Tool for MCP
  The project’s minimalistic approach makes it ideal for learning about the Model Context Protocol and experimenting with LLM connectivity.

How to set it up

Windsurf

1. Install Node.js: Download and install Node.js from nodejs.org.
2. Clone the repository:
   git clone https://github.com/AI-QL/chat-mcp.git
3. Edit configuration:
   Modify src/main/config.json with your LLM API details and MCP settings.
4. Install dependencies:
   npm install
5. Start the app:
   npm start

Example JSON configuration:

{
    "chatbotStore": {
        "apiKey": "",
        "url": "https://api.aiql.com",
        "path": "/v1/chat/completions",
        "model": "gpt-4o-mini",
        "mcp": true
    }
}

Note: Secure your API keys by using environment variables or encrypted storage (not directly supported in provided config, but recommended).

Claude

1. Install Node.js: Obtain Node.js from nodejs.org.
2. Download/clone Chat MCP.
3. Edit src/main/config.json with Claude-compatible API endpoint and details.
4. Run npm install.
5. Launch with npm start.

Example JSON:

{
    "chatbotStore": {
        "apiKey": "",
        "url": "https://anthropic.api.endpoint",
        "path": "/v1/messages",
        "model": "claude-3-opus",
        "mcp": true
    }
}

Note: Use environment variables for sensitive data.

Cursor

1. Install Node.js.
2. Clone Chat MCP repository.
3. Update src/main/config.json for Cursor backend.
4. Install dependencies.
5. Start application.

Example JSON:

{
    "chatbotStore": {
        "apiKey": "",
        "url": "https://cursor.api.endpoint",
        "path": "/v1/chat/completions",
        "model": "cursor-model",
        "mcp": true
    }
}

Note: Use environment variables for API keys.

Cline

1. Install Node.js.
2. Clone the repository.
3. Edit src/main/config.json for Cline API details.
4. Run npm install.
5. Start with npm start.

Example JSON:

{
    "chatbotStore": {
        "apiKey": "",
        "url": "https://cline.api.endpoint",
        "path": "/v1/chat/completions",
        "model": "cline-model",
        "mcp": true
    }
}

Note: Secure API keys using environment variables.

Securing API Keys Example:

{
    "chatbotStore": {
        "apiKey": "${API_KEY}",
        "url": "https://api.example.com",
        "path": "/v1/chat/completions",
        "model": "your-model",
        "mcp": true
    }
}

Set API_KEY in your environment before starting the app.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "chat-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “chat-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the available information, Chat MCP is a simple, educational, and flexible MCP client, but lacks advanced MCP features (tools, resources, sampling, roots) in its public documentation and setup. Its main value is as a clean, modifiable chat front-end. Overall, it is a good starting point for MCP learning or as a base for more advanced integrations.

MCP Score