MCP Multi-Service Agent

A collection of Model Context Protocol (MCP) servers providing various AI-powered services, designed to work with Hugging Face's tiny-agents framework.

Features

Core Services

• Weather Service: Get weather information for any location

• Web Search: Search the web for information (with special support for Hugging Face inference providers)

• Sentiment Analysis: Analyze text sentiment with polarity and subjectivity scores

Integration Features

• AI Agent Integration: Works seamlessly with tiny-agents for conversational AI

• MCP Inspector Support: Debug and inspect server capabilities

• Gradio Web Interface: Interactive web UI for sentiment analysis

Prerequisites

• Python 3.10 or higher

• uv package manager

• Node.js (for MCP inspector)

• Hugging Face account (for tiny-agents)

Installation

1. Clone or download this project

   git clone https://github.com/Deon62/mcp.git
   cd mcps

2. Install Python dependencies

   uv pip install mcp[cli] requests

3. Install tiny-agents (if not already installed)

   pip install tiny-agents

4. Install sentiment analysis dependencies

   cd mcp-sentiment
   python -m venv venv
   venv\Scripts\activate  # On Windows
   # source venv/bin/activate  # On Linux/Mac
   pip install -r requirements.txt

Quick Start

1. Run the MCP Server

Start the MCP server in one terminal:

uv run --with mcp mcp run server.py

The server will start and wait for connections.

2. Run the AI Agent

In another terminal, start the agent:

tiny-agents run agent.json

You should see:

Agent loaded with 3 tools:
 • get_weather
 • web_search
 • get_hf_inference_providers
»

3. Run Sentiment Analysis Web App

For the sentiment analysis service, run the Gradio web interface:

cd mcp-sentiment
venv\Scripts\activate  # On Windows
# source venv/bin/activate  # On Linux/Mac
python app.py

The web interface will be available at http://localhost:7860

4. Chat with the Agent

Once the agent is running, you can interact with it:

» Hello! Can you help me find information about Hugging Face inference providers?

Available Tools

1. Weather Service

» What's the weather like in New York?

2. Web Search

» Search for "Hugging Face inference providers"

3. HF Inference Providers

» Get me the list of Hugging Face inference providers

4. Sentiment Analysis

» Analyze the sentiment of this text: "I love this new product!"

Web Interface Features:

• Polarity Score: -1 (negative) to +1 (positive)

• Subjectivity Score: 0 (objective) to 1 (subjective)

• Assessment: Positive, Negative, or Neutral classification

• Real-time Analysis: Instant sentiment analysis as you type

Configuration

Agent Configuration (agent.json)

{
    "model": "Qwen/Qwen2.5-72B-Instruct",
    "provider": "nebius",
    "servers": [
        {
            "type": "stdio",
            "command": "uv",
            "args": ["run", "--with", "mcp", "mcp", "run", "server.py"]
        }
    ]
}

Server Configuration (server.py)

The server provides three main tools:

• get_weather(location) - Returns weather information

• web_search(query) - Performs web searches

• get_hf_inference_providers() - Returns comprehensive list of HF inference providers

MCP Inspector Setup

The MCP Inspector allows you to debug and test your MCP server directly.

1. Install MCP Inspector

npm install -g @modelcontextprotocol/inspector

2. Run the Inspector

mcp-inspector

3. Connect to Your Server

In the inspector:

1. Click "Add Server"

2. Choose "stdio" transport

3. Set command: uv

4. Set args: ["run", "--with", "mcp", "mcp", "run", "server.py"]

5. Click "Connect"

4. Test Tools

Once connected, you can:

• View available tools in the sidebar

• Test each tool with different parameters

• See the JSON-RPC communication

• Debug any issues

Example Usage

Weather Queries

» What's the weather in Tokyo?
» Get weather for London
» How's the weather in San Francisco?

Web Search Queries

» Search for "latest AI developments"
» Find information about "MCP protocol"
» Look up "Hugging Face inference providers"

Specific HF Provider Queries

» Show me all Hugging Face inference providers
» What inference providers does HF support?
» List the available HF deployment options

Sentiment Analysis Examples

» Analyze the sentiment of: "This product is amazing and I love it!"
» What's the sentiment of: "I'm not sure about this decision"
» Check the sentiment of: "The weather is okay today"

Example Output:

{
  "polarity": 0.8,
  "subjectivity": 0.9,
  "assessment": "positive"
}

Troubleshooting

Common Issues

1. "ModuleNotFoundError: No module named 'mcp'"

   uv pip install mcp[cli]

2. "KeyError: 'command'"

   • Check your agent.json configuration

   • Ensure the server configuration is correct

3. "Connection closed" errors

   • Make sure the MCP server is running

   • Check that all dependencies are installed

4. Agent shows "0 tools"

   • Verify the server is running

   • Check the agent.json configuration

   • Ensure the server command is correct

5. Sentiment Analysis Import Errors

   • Ensure virtual environment is activated

   • Install NLTK data: python -c "import nltk; nltk.download('punkt'); nltk.download('brown')"

   • Check TextBlob installation: python -c "from textblob import TextBlob; print('OK')"

6. Gradio Interface Issues

   • Update Gradio: pip install --upgrade gradio

   • Check for port conflicts (default: 7860)

   • Verify MCP server parameter compatibility

Debug Steps

1. Test the server directly:

   python server.py

2. Check MCP server with inspector:

   mcp-inspector

3. Verify dependencies:

   uv pip list | grep mcp

4. Test sentiment analysis:

   cd mcp-sentiment
   venv\Scripts\activate
   python -c "from textblob import TextBlob; print(TextBlob('Hello world').sentiment)"

Project Structure

mcps/
├── server.py              # Main MCP server implementation
├── agent.json             # Agent configuration
├── requirements.txt       # Python dependencies
├── uv.lock               # Dependency lock file
├── app.py                # Main application entry point
├── mcp-sentiment/        # Sentiment analysis service
│   ├── app.py            # Gradio web interface for sentiment analysis
│   ├── requirements.txt  # Sentiment analysis dependencies
│   └── venv/             # Virtual environment for sentiment analysis
└── README.md             # This file

Development

Adding New Tools

To add a new tool to the server:

@mcp.tool()
def your_new_tool(param: str) -> str:
    """Description of what this tool does"""
    return f"Result for {param}"

Modifying Agent Configuration

Edit agent.json to:

• Change the AI model

• Add more MCP servers

• Modify server configurations

Hugging Face Inference Providers

The server includes comprehensive information about HF inference providers:

1. Amazon SageMaker - Serverless inference with custom Inferentia2 chips

2. Novita AI - Integrated serverless inference directly on model pages

3. Together AI - Serverless inference with competitive pricing

4. Nscale - Official HF provider with high-performance GPU clusters

5. Inference Endpoints - Dedicated, fully managed infrastructure

6. Google Cloud - Vertex AI and other deployment options

7. Microsoft Azure - Azure Machine Learning services

8. Replicate - Easy-to-use model deployment platform

9. Banana - Serverless GPU inference platform

10. Modal - Serverless compute platform

11. RunPod - GPU cloud computing

12. Lambda Labs - GPU cloud infrastructure

Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Test with both the agent and inspector

5. Submit a pull request

License

[Add your license information here]

Support

For issues and questions:

1. Check the troubleshooting section

2. Use the MCP inspector to debug

3. Open an issue on GitHub

4. Check the MCP documentation

**Happy coding! **