MCP Servers

This project connect MCP server using http (ip or port) for local mcp server connection using langchain. After connection fetching all tools from server and passed it to agent creation along with llm.

Model Context Protocol, or MCP, is an open protocol that standardizes how applications provide context to LLMs. In other words it provides a unified framework for LLM based applications to connect to connect to data sources, get context, use tools, and execute standard prompts.

The MCP ecosystem outlines three specific components:

MCP Servers handle: tool availability (exposing what functions are available), tool execution (running those functions when requested), static content as resources (providing data that can be referenced), preset prompts (standardized templates for common tasks)

Clients manage: Connections to servers, LLM integration, message passing between components

Hosts provide: Frontend interfaces, surfacing of MCP functionality to users, integration points for the overall ecosystem

1. Clone the Repo git clone https://github.com/ramandahiya/MCP.git

2. Create a Virtual Environment Creating a virtual environment helps isolate dependencies and prevents conflicts with other Python projects.

   For Windows (Command Prompt) python -m venv my-env my-env\Scripts\activate For macOS/Linux (Terminal) python -m venv my-env source my-env/bin/activate

3. Install Requirements Once the virtual environment is activated, install the required dependencies. pip install -r requirements.txt

4. Define groq api key in .env file GROQ_API_KEY = ###

5. Run mcp server Run mcp.py

6. Run agent / MCP client Run agent.py prompt and cotext is hardcoded in this file.