Unichat MCP Server in Python: Enhancing AI Application Integration

Overview: What is Unichat MCP Server?

The Unichat MCP (Model Context Protocol) Server is a powerful tool designed to facilitate seamless integration of various AI applications with external data sources and tools. By leveraging the Model Context Protocol, this server ensures compatibility across different platforms and enhances the functionality of AI models like Claude Desktop, Continue, Cursor, and more. The OpenAI, MistralAI, Anthropic, xAI, Google AI, DeepSeek, Alibaba, Inception, and other vendors are supported, making it a versatile solution for developers looking to build robust and interoperable AI systems.

🔧 Core Features & MCP Capabilities

The Unichat MCP Server offers several core capabilities that make it an essential component in modern AI ecosystems. These features include:

• Tool Integration: The server supports the execution of various tools through MCP, allowing users to send requests such as code reviews or documentation generation directly from supported AI clients.

  • Example:

    unichat(messages="Send a request to Unichat")
    

• Prompt-Based Interactions: Users can interact with the server via predefined prompts that trigger specific actions, such as reviewing code, generating documentation, explaining code functionality, or even reworking existing code.

  • Example Prompts:
    • code_review: Reviews provided code for best practices and potential issues.
    • document_code: Generates documentation based on the given code, including docstrings and comments.
    • explain_code: Expands on how a specific piece of code works in detail.
    • code_rework: Applies changes to the existing code.

• Vendor API Key Support: To function properly, the server requires an API key from vendors such as OpenAI, MistralAI, Anthropic, xAI, Google AI, DeepSeek, Alibaba, and Inception. This ensures controlled access and maintains privacy while facilitating seamless interactions with these powerful tools.

⚙️ MCP Architecture & Protocol Implementation

The architecture of the Unichat MCP Server is built to ensure compatibility and maintainability across various environments. Here's a quick overview:

• Tool Implementation: The server implements one primary tool, unichat, which allows users to send requests to Unichat for various purposes.

  • Example:

    unichat(messages="Send a request to Unichat")
    

• Prompt Handling: It supports specific prompts like code_review, document_code, explain_code, and code_rework. Each prompt is designed to handle different tasks, such as code analysis, documentation generation, or reworking existing code.

  • Example Prompts:
    • code_review: Reviews the provided code for best practices.
    • document_code: Generates documentation based on the given code.
    • explain_code: Explains how a piece of code works in detail.
    • code_rework: Applies changes to the existing code.

• Vendor API Key Configuration: The server configuration includes support for vendors' API keys, ensuring that all interactions remain secure and effective. Users must adhere to proper key management practices to maintain smooth operations.

🚀 Getting Started with Installation

Install

Supported Models

The following models are currently supported:

• GPT-4o-mini
• Davinci
• Other OpenAI models
• Anthropic, MistralAI, xAI, Google AI, DeepSeek, Alibaba, Inception models

Example Configuration:

"env": {
  "UNICHAT_MODEL": "gpt-4o-mini",
  "UNICHAT_API_KEY": "YOUR_OPENAI_API_KEY"
}

Development/Unpublished Servers Configuration

For development purposes or for servers that are not yet published, you can configure the MCP server in your client settings like so:

"mcpServers": {
  "unichat-mcp-server": {
    "command": "uv",
    "args": [
      "--directory",
      "{{your source code local directory}}/unichat-mcp-server",
      "run",
      "unichat-mcp-server"
    ],
    "env": {
      "UNICHAT_MODEL": "SELECTED_UNICHAT_MODEL",
      "UNICHAT_API_KEY": "YOUR_UNICHAT_API_KEY"
    }
  }
}

Published Servers Configuration

For published servers, the configuration is slightly different:

"mcpServers": {
  "unichat-mcp-server": {
    "command": "uvx",
    "args": [
      "unichat-mcp-server"
    ],
    "env": {
      "UNICHAT_MODEL": "SELECTED_UNICHAT_MODEL",
      "UNICHAT_API_KEY": "YOUR_UNICHAT_API_KEY"
    }
  }
}

Installing via Smithery

To install Unichat for Claude Desktop automatically, use the following command:

npx -y @smithery/cli install unichat-mcp-server --client claude

💡 Key Use Cases in AI Workflows

Real-world Workflow Scenario 1: Code Review

Imagine a development team using Unichat MCP Server to perform routine code reviews. They can send snippets of code directly from their AI client and receive detailed feedback, ensuring the quality of the software they develop.

Real-world Workflow Scenario 2: Documentation Generation

A content creator might use the document_code prompt to generate documentation automatically based on a piece of code they have written. This saves time and helps maintain consistency across projects while ensuring that all necessary documentation is always up-to-date.

🔌 Integration with MCP Clients

The Unichat MCP Server supports integration with various AI clients, including Claude Desktop, Continue, Cursor, and others. Here’s the current compatibility matrix:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	❌	Tools Only
Cursor	❌	✅	❌	Tools Only

📊 Performance & Compatibility Matrix

The server has been rigorously tested to ensure optimal performance and compatibility with various environments. While the primary focus is on supporting key clients, some tools might only be partially supported currently.

🛠️ Advanced Configuration & Security

MCP Protocol Flow Diagram

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

MCP Configuration Sample

{
  "mcpServers": {
    "unichat-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "{{your source code local directory}}/unichat-mcp-server",
        "run",
        "unichat-mcp-server"
      ],
      "env": {
        "UNICHAT_MODEL": "SELECTED_UNICHAT_MODEL",
        "UNICHAT_API_KEY": "YOUR_UNICHAT_API_KEY"
      }
    }
  }
}

❓ Frequently Asked Questions (FAQ)

1. Q: Does Unichat MCP Server support all AI clients?

   • A: Yes, it supports several prominent AI applications like Claude Desktop and Continue. For full compatibility, please refer to the server's official documentation.

2. Q: Can I run this server on Windows or MacOS?

   • A: Absolutely! The server is compatible with both Windows and macOS environments, ensuring seamless integration across different operating systems.

3. Q: How do I configure API keys for various vendors?

   • A: You need to add the relevant vendor API key as "YOUR_UNICHAT_API_KEY" in your environment setup to ensure secure interactions with the server.

4. Q: What tools are supported out-of-the-box?

   • A: The following tools are currently available:
     • Review code for best practices
     • Generate documentation based on provided code
     • Explain how a piece of code works
     • Apply changes to existing code

5. Q: Can I debug the server directly from my AI client?

   • A: While direct debugging is challenging, we recommend using the MCP Inspector tool, which provides detailed insights and easier troubleshooting.

👨‍💻 Development & Contribution Guidelines

Building and Publishing

To prepare the package for distribution:

1. Remove older builds:

   rm -rf dist
   

2. Sync dependencies and update lockfile:

   uv sync
   

3. Build package distributions:

   uv build
   

This will create source and wheel distributions in the dist/ directory.

4. Publish to PyPI:

   uv publish --token {{YOUR_PYPI_API_TOKEN}}
   

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory {{your source code local directory}}/unichat-mcp-server run unichat-mcp-server

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

🌐 MCP Ecosystem & Resources

Explore further by visiting our community forums and documentation pages:

• GitHub Repository
• MCP Protocol Documentation

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By leveraging the Unichat MCP Server, developers can significantly enhance their AI application's capabilities, ensuring seamless integration with diverse tools and resources. Embrace the power of MCP and transform your projects for a more connected and intelligent future!