Native MCP Client Server

@hackerinheels

Basic information

About Native MCP Client Server

A native implementation of the MCP (Model Context Protocol) client-server architecture with HTTP Server-Sent Events (SSE) for real-time communication.

Native MCP Client Server config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

Repository

Native MCP Client Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Native MCP Client Server?

A native implementation of the Machine Conversation Protocol (MCP) using HTTP Server-Sent Events (SSE) for real-time communication between a WebSocket-based host and multiple tool servers. It provides a host, product server, analytics server, and client for building multi-service tool systems.

How to use Native MCP Client Server?

Set up a Python virtual environment, install dependencies (httpx, fastapi, uvicorn, websockets, python-dotenv, requests, PyYAML), and configure .env files for the host and each tool server. Start the tool servers (e.g., python product-server/server_product.py and python analytics-server/server_analytics.py), then run python mcp-host.py. Send messages like "get products" or "show analytics" to interact.

Key features of Native MCP Client Server

Tool discovery via HTTP endpoints
Real-time tool execution with SSE
WebSocket-based client-host communication
Support for Ollama and Gemini LLM backends
Multiple tool servers (Product and Analytics)
Dynamic tool discovery from all configured servers

Use cases of Native MCP Client Server

Query product information from a dedicated server via the "get products" command
Retrieve analytics data for daily, weekly, or monthly periods using "get analytics"
Combine data from multiple tool servers through a single MCP Host
Extend the system by adding custom tool servers with new /tools and /run endpoints

Frequently asked questions

What runtime dependencies are required?

Python 3 and packages: httpx, fastapi, uvicorn, websockets, python-dotenv, requests, PyYAML. Optionally Ollama or Gemini for the LLM backend.

How does the system communicate?

The MCP Host communicates with clients via WebSocket and with tool servers via HTTP using Server-Sent Events (SSE) for real‑time responses.

How can I add a new tool server?

Create a server with `/tools` and `/run` endpoints, add its URL and script path to `config.yaml`, and update the

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