Agent-with-MCP-connection

Amin-alzz96/Agent-with-MCP-connection

3.2

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This project demonstrates a proof-of-concept for connecting a LangChain agent to a custom Model Context Protocol (MCP) tool, which is a web scraper that extracts data from websites using CSS selectors.

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LangChain Agent with MCP Web Scraper Tool

This project demonstrates a proof-of-concept (POC) for connecting a LangChain (or LangGraph) agent to a custom Model Context Protocol (MCP) tool. The MCP tool is a web scraper that extracts data from any website using a CSS selector. The agent can invoke this tool as part of its reasoning process.

Features

  • MCP Server: Exposes a web scraping tool via MCP protocol.
  • LangChain Agent: Connects to the MCP tool and an LLM (OpenAI GPT-4o-mini by default).
  • Async Integration: Uses async agent and tool invocation for compatibility.
  • Configurable: Server configuration via conf.json.

Quick Start Guide

1. Prerequisites

  • Python 3.12+
  • uv for dependency management (or use pip if preferred)
  • OpenAI API key (for LLM)

2. Clone the Repository

git clone <your-repo-url>
cd 08_MCP_Agent

3. Install Dependencies

uv pip install -r requirements.txt
# OR, if using pyproject.toml:
uv sync

4. Set Up Environment Variables

Create a .env file in the project root:

OPENAI_API_KEY=your-openai-api-key

5. Run the Agent

In another terminal:

python main.py

You should see the agent invoke the web scraper tool and print the results.

Project Structure

  • main.py — Entry point; runs the async agent and connects to the MCP tool.
  • mcp_server.py — Defines and serves the web scraping MCP tool.
  • llm_config.py — LLM (OpenAI) configuration.
  • conf.json — MCP server configuration.
  • pyproject.toml — Project dependencies.
  • README.md — This file.

How It Works

  1. MCP Server: mcp_server.py registers a scrape_website tool that takes a URL and CSS selector, returning the text and attributes of matching elements. this app is designed to run locally so please make necessary changes in conf.json file.
  2. Agent: main.py loads the MCP tool using langchain_mcp_adapters, connects it to the LLM, and runs an async agent loop.
  3. Example: The agent is prompted to scrape all <h1> elements from a sample website.

Customization

  • To add more MCP tools, define them in mcp_server.py and update conf.json.
  • To use a different LLM, modify llm_config.py.

License

MIT