Microservice Control Panel (MCP)

Integrations

Enables configuration management through environment variables, allowing customization of the MCP server's behavior
Provides the API backend for the MCP server, allowing HTTP-based interactions with the system's microservices
Supports Jupyter notebook execution within the MCP environment, allowing notebook-based workflows for AI applications

Model Context Protocol (MCP)

Overview

MCP is a modular framework for managing, executing, and monitoring AI model contexts, including LLM prompts, Jupyter notebooks, and Python scripts. It provides a FastAPI backend and a Streamlit dashboard frontend.

Features

Register and manage different types of MCPs (LLM prompts, notebooks, scripts)
Execute MCPs and view results in a web UI
Monitor server health and statistics
Extensible for new MCP types

Setup

Prerequisites

Python 3.9+
(Recommended) Create and activate a virtual environment

Install dependencies

pip install -r requirements.txt

Environment Variables

Set MCP_API_KEY for API authentication (optional, defaults provided)
For LLMs, set ANTHROPIC_API_KEY if using Claude

Start the backend

uvicorn mcp.api.main:app --reload

Start the frontend

streamlit run mcp/ui/app.py

Usage

Access the dashboard at http://localhost:8501
Create, manage, and test MCPs from the UI
Monitor health and stats from the sidebar

Adding New MCPs

Implement a new MCP class in mcp/core/
Register it in the backend
Add UI support in mcp/ui/app.py

Running Tests

pytest

Project Structure

mcp/api/ - FastAPI backend
mcp/ui/ - Streamlit frontend
mcp/core/ - Core MCP types and logic
tests/ - Test suite

License

MIT

API Documentation

Once the server is running, you can access:

API documentation: http://localhost:8000/docs
Prometheus metrics: http://localhost:8000/metrics
Health check: http://localhost:8000/health
Statistics: http://localhost:8000/stats

Security

API key authentication is required for all endpoints
Rate limiting is enabled by default
CORS is configured to allow only specific origins
All sensitive configuration is managed through environment variables

Monitoring

The server includes:

Prometheus metrics for request counts, latencies, and server executions
Structured JSON logging
Health check endpoint
Server statistics endpoint

Contributing

Fork the repository
Create a feature branch
Commit your changes
Push to the branch
Create a Pull Request

Additional Dependencies for Notebook and LLM Integration

This project now requires the following additional Python packages:

pandas
numpy
matplotlib
papermill
nbformat
jupyter
anthropic

Install all dependencies with:

pip install -r requirements.txt

Using the Notebook MCP to Call an LLM (Claude)

The example notebook (mcp/notebooks/example.ipynb) demonstrates:

Data analysis and plotting
Calling the Claude LLM via the anthropic Python package

To use the LLM cell, ensure you have set your ANTHROPIC_API_KEY in your environment or .env file.

The notebook cell for LLM looks like this:

import os
import anthropic

api_key = os.getenv('ANTHROPIC_API_KEY')
if not api_key:
    raise ValueError('ANTHROPIC_API_KEY not set in environment!')

client = anthropic.Anthropic(api_key=api_key)
response = client.messages.create(
    model='claude-3-sonnet-20240229',
    max_tokens=256,
    temperature=0.7,
    messages=[
        {'role': 'user', 'content': 'Tell me a joke about data science.'}
    ]
)
print('Claude says:', response.content[0].text)

This server cannot be installed

security - not tested

license - not found

quality - not tested

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

A modular system for building and orchestrating AI applications through microservices, featuring LLM interactions, Jupyter notebook execution, and visual workflow capabilities.

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