🧠 Model Context Protocol (MCP)
Demo of implementation of MCP using Langchain MCP Adapters and Ollama
What is Model Context Protocol (MCP)?
Model Context Protocol (MCP) is an open-source protocol designed to connect Large Language Models (LLMs) with external tools, services, and data sources, acting as a translator layer for seamless integration.
How to use MCP?
To use MCP, start the servers (mathserver and weatherserver) and run the client or multi-client demo scripts to interact with the LLMs and external services.
Key features of MCP?
- Standardized interface for tool integration
- Pre-built integrations for LLMs
- Flexibility to switch between LLM providers
- Best practices for data security
Use cases of MCP?
- Building AI assistants that can perform real-world tasks
- Integrating LLMs with databases and APIs
- Creating complex workflows using LLMs and external tools
FAQ from MCP?
- What is the purpose of MCP?
MCP simplifies the integration of LLMs with various tools and services, making it easier to build powerful AI applications.
- Is MCP open-source?
Yes! MCP is an open-source protocol available for anyone to use and contribute to.
- How does MCP ensure data security?
MCP follows best practices for securing data within your infrastructure, ensuring safe communication between clients and servers.
🧠 Model Context Protocol (MCP)
An open-source standard to seamlessly connect Large Language Models (LLMs) with the external world — databases, APIs, services, and more.
🌐 What is MCP?
Model Context Protocol (MCP) is a new open-source protocol designed to empower LLMs by enabling them to interface with external tools, services, and data sources. Acting as a translator layer, MCP allows models to interact with APIs, databases, and other services in a standardized, extensible, and scalable way.
🚨 The Problem
LLMs alone can't execute real-world tasks — they only generate text. To build powerful AI assistants, we need to integrate them with tools like:
- Email services
- Search APIs
- Databases
- Custom scripts
But integrating multiple tools is hard. APIs vary widely, maintenance is a headache, and scalability is painful.
✅ MCP as a Solution
MCP provides a standardized interface that abstracts away the complexities of tool integration. Similar to how REST standardized web services, MCP standardizes how LLMs talk to tools — making integration cleaner, easier, and future-proof.
🔮 Why MCP?
MCP helps you build agents and complex workflows on top of LLMs. LLMs frequently need to integrate with data and tools, and MCP provides:
- A growing list of pre-built integrations that your LLM can directly plug into
- The flexibility to switch between LLM providers and vendors
- Best practices for securing your data within your infrastructure
🧩 Architecture & Components
| Component | Description |
|---|---|
| MCP Client | The LLM-facing component. Can reside in chat apps, dev tools, or assistants. |
| MCP Server | Built by service providers. Translates service functionality (e.g., database queries, API calls) into a format LLMs can understand. |
| MCP Protocol | The two-way transport layer enabling secure, structured communication between client and server. |
| Service | The actual tool or external resource being accessed (e.g., Weather API, SQL DB). |
🔁 How It Works (Flow Example)
- User sends query via an MCP host (e.g., chat app).
- MCP Client identifies the need for an external tool.
- MCP Server advertises available tools.
- LLM decides which tool(s) to use and instructs the client.
- Client sends request to relevant MCP Server.
- Server connects to the external service and retrieves data.
- Response flows back to the LLM for final output generation.
✨ Key Benefits
- ✅ Simplified Tool Integration
- 🚀 Extended LLM Capabilities
- 🛠️ Scalable, Maintainable Architecture
- 🤝 Standardized Communication Layer
- 💡 Fosters Innovation for AI App Developers
🛠️ Tech Stack Used
- Python - Python forms the backbone of CodeBuddy, providing robust support for integration with various libraries and frameworks.
- Langchain - LangChain is a framework designed to simplify the creation of applications using large language models.
- Ollama - It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.
- langchain-mcp-adapters - This library provides a lightweight wrapper that makes Anthropic Model Context Protocol (MCP) tools compatible with LangChain and LangGraph.
🧩 Files Overview
| File | Description |
|---|---|
| client.py | A basic MCP client interacting only with a single mathserver. |
| mathserver.py | An MCP server that exposes simple math operations (e.g., addition, multiplication). |
| weatherserver.py | An MCP server simulating weather data responses. |
| multiclient.py | A multi-client setup where the MCP client can connect to both the math and weather servers. |
🔄 How It Works
-
The client.py script simulates an AI assistant (or LLM) interacting with the Math MCP Server only.
-
The multiclient.py script demonstrates a more advanced use-case where the MCP client discovers and uses tools from multiple servers (Math + Weather).
-
mathserver.py and weatherserver.py expose capabilities that can be consumed by MCP clients.
🚀 Running the Demo
-
Start the Servers:
python mathserver.py python weatherserver.py -
Run Single-Client Demo:
python client.py -
Run Multi-Client Demo:
python multiclient.py
🚀 Get Involved
Contributions are welcome! If you have suggestions or would like to enhance this project, please fork the repository and submit a pull request.
Interested in contributing to MCP? Stay tuned for:
- Contribution guidelines
- Roadmap
- Issue templates
Feel free to ⭐️ the repo and join the discussion!
License
This project is licensed under the MIT License. See the LICENSE file for more details.
