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MCP-Servers

This project showcases the use of Modern Context Protocol (MCP) to enable tools using agents to interact with structured business data (Edu Tech Platform) and available mcp tools like duckduckgo search, airbnb etc...

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System Overview

What the project does

A dual‑example repository that demonstrates how to build, register, and consume Multi‑Component Processor (MCP) servers:

1. An HR‑management API built with FastMCP, auto‑registered in Claude Desktop for natural‑language queries.

2. A memory‑enabled chat demo that orchestrates three external MCP tools (Playwright, Airbnb, DuckDuckGo) via LangChain’s MCP framework, using a Groq LLM.

Key features

  • Decorator‑based endpoints (`@mcp.tool()`, `@mcp.resource()`) with realistic employee data and robust error handling.
  • One‑click install (`uv run mcp install main.py`) that updates Claude Desktop’s `config.json`.
  • Configurable MCP host/client architecture (`browser_mcp.json`) to launch external MCP servers with `npx`.
  • Async chat loop with persistent memory, clear/exit commands, and live preview.
  • Fully reproducible environment via **uv**, virtual‑env, and lock files.

    • Tech stack

  • **Python 3.10+**, **uv** (package manager)
  • **FastMCP**, **LangChain‑MCP**, **asyncio**
  • **Groq LLM** (`ChatGroq`, model `qwen-qwq-32b`)
  • **Claude Desktop**, **Cursor IDE** (MCP host)
  • **Node.js / npx** for external MCP servers (`@playwright/mcp`, `@openbnb/mcp-server-airbnb`, `duckduckgo-mcp-server`)
  • Supporting libs: `python-dotenv`, `langchain_groq`

    • Use case

    Ideal as a reference implementation for developers who need to expose custom business logic (e.g., HR operations) to AI assistants, and for building multi‑tool conversational agents that combine search, automation, and domain‑specific APIs through a unified MCP interface.

    Architecture Details

    This system integrates multiple components for a seamless automation flow. Structural interpretation based on project focus:

    Backend Infrastructure

    Core execution layer for robust data processing and API handling.

    AI / Logic Core

    Intelligent decisioning via models or logical workflow rules.

    Tech Stack

    PythonIntegrationAutomationAPIs

    Key Capabilities

    • ▹ Custom workflow execution
    • ▹ Data transformation and routing
    • ▹ Extensible architecture