How does Model Context Server handle real-time data?

It utilizes Server-Sent Events (SSE) for real-time streaming responses, enabling dynamic and interactive communication for LLM completions and other data.

What Large Language Models (LLMs) can I use with it?

The Model Context Server seamlessly integrates with both Azure OpenAI services and standard OpenAI models, providing flexible LLM access for your applications.

What security features does it offer?

It uses JWT-based authentication for secure access, coupled with environment-based configuration and input validation to protect your application.

What is the Model Context Server?

It's a comprehensive Python backend implementing the Model Context Protocol (MCP), designed for building AI applications with LLM integration and real-time streaming capabilities.

Can I extend the server's functionality with custom logic?

Yes, the server features an extensible tool registry allowing you to add custom tools and integrate custom resource handlers, making it highly adaptable to specific needs.

Model Context Server

Name: Model Context Server
Author: Krishanu-das-05

Implements the Model Context Protocol (MCP) as a comprehensive Python backend, integrating JSON-RPC 2.0, Azure OpenAI, and Server-Sent Events for streaming responses.

概要

This Python backend provides a robust implementation of the Model Context Protocol (MCP), enabling seamless communication with AI models through JSON-RPC 2.0. It features deep integration with Azure OpenAI for powerful language model interactions, alongside real-time streaming capabilities via Server-Sent Events. The server also includes extensive resource management, an extensible tool execution registry, JWT authentication, and Prometheus monitoring, making it a comprehensive solution for building AI-powered applications.

主な機能

Azure OpenAI and Standard OpenAI Integration
Extensible Tool Execution and Resource Management
0 GitHub stars
Complete MCP Protocol Support
Real-time Server-Sent Events (SSE) Streaming
JWT Authentication and Prometheus Monitoring

ユースケース

Building AI-powered applications requiring structured communication with large language models (LLMs).
Integrating Azure OpenAI services with custom backend logic and real-time data streams.
Developing systems that require tool execution and file system resource access coordinated via an AI protocol.