MinimalMCP

MinimalMCP is designed to facilitate seamless interaction between clients and machine learning models using the Model Context Protocol. By utilizing minimal APIs, it ensures that the server remains lightweight and efficient, reducing overhead and improving response times. This makes it an ideal choice for applications that require quick and reliable access to model predictions or data processing. The server is built to be easily integrated into existing systems, providing a flexible and scalable solution for developers looking to implement MCP in their projects.

Features

• Lightweight Architecture: Utilizes minimal APIs to ensure low overhead and fast response times.
• Scalable: Easily integrates into existing systems and scales with demand.
• Flexible: Supports a wide range of machine learning models and applications.
• Efficient Communication: Streamlines the interaction between clients and models.
• Easy Integration: Designed to be easily incorporated into various development environments.

Usage with Different Platforms

Python

python
import requests

url = 'http://localhost:5000/predict'
data = {'input': 'your input data'}
response = requests.post(url, json=data)
print(response.json())

Node.js

javascript
const axios = require('axios');

axios.post('http://localhost:5000/predict', {
    input: 'your input data'
})
.then(response => {
    console.log(response.data);
})
.catch(error => {
    console.error('Error:', error);
});

Curl

bash
curl -X POST http://localhost:5000/predict -H "Content-Type: application/json" -d '{"input": "your input data"}'