MCP Projects

The MCP project is a standardized protocol for enhancing the context understanding ability of AI models. It provides environmental information, user preferences, and conversation history in a structured manner, solving the problem of limited memory in AI systems. The project includes installation guides, environment configuration, and multiple experimental cases.

Knowledge management and memory Artificial intelligence chatbots #AI enhancement #Context management #Protocol standard .Python

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update time : 2025-04-29

What is the MCP server?

The MCP server is an intelligent context management system that provides environmental information, user preferences, and conversation history to AI models in a structured manner. It solves the problem of limited 'working memory' in AI systems, making conversations more coherent and natural.

How to use the MCP server?

Through simple API integration, developers can connect the MCP server to existing AI systems. The system will automatically manage the context window and optimize the model's input and output.

Applicable scenarios

It is suitable for scenarios such as conversation systems requiring long - term memory, personalized recommendation engines, and enterprise knowledge management systems. It is particularly suitable for applications that need to maintain context across conversations.

Main features

Intelligent context managementAutomatically organize and optimize the context information provided to AI models, breaking through the limitations of traditional context windows.

Support for multiple model providersSupports multiple LLM providers such as IBM Watsonx.ai and allows flexible switching between different AI models.

Web search integrationIntegrate real - time web search capabilities through the Tavily API to enhance the model's knowledge base.

Advantages and limitations

Advantages

Solve the 'amnesia' problem in AI systems and provide long - term memory capabilities

Standardized interface design, easy to integrate into existing systems

Support mainstream AI model providers, with flexible choices

Improve the quality of model responses through structured context

Limitations

Requires additional infrastructure support

There is a certain threshold for non - technical users to configure

Performance may be affected by the amount of context data

How to use

Clone the repository

Get the source code of the MCP server

Install dependencies

Install the necessary Python packages

Configure environment variables

Create a.env file and set authentication information such as API keys

Start the server

Run the MCP server

Connect the client

Develop or use a compatible client application to connect to the server

Usage examples

Personalized conversation systemBuild a chatbot that can remember user preferences

Enterprise knowledge baseCreate an enterprise knowledge Q&A system with long - term memory

Frequently Asked Questions

Does MCP depend on a specific AI model?

How much technical knowledge is required to deploy MCP?

How does MCP handle private data?

Related resources

Detailed explanation of the MCP protocol (Medium article)

Plainly explain the principle and application of the MCP protocol

GitHub repository

Project source code and examples

FastAPI documentation

The Web framework used by the MCP server

🚀 mcp-projects

This is the repository for my MCP (Model Context Protocol) project, which aims to provide a standardized way for applications to offer richer context to AI models.

🚀 Quick Start

📦 Installation

Clone this repository.
Install the dependencies:

pip install mcp
pip install fastapi
pip install uvicorn
pip install fastapi-mcp
pip install llama-index
pip install llama-index-embeddings-huggingface
pip install llama-index-llms-langchain
pip install langchain-mcp-adapters
pip install mcp-use

Create a .env file in the root folder with the following credentials:

API_KEY=<IBM Cloud API Key>
PROJECT_ID=<Watsonx Project ID>
IBM_CLOUD_URL=<IBM Cloud URL>

MODEL_ID=<Your Watsonx.ai LLM ID>

TAVILY_API_KEY=<Your Tavily Web Search API Key>

Alternatively, use your own LLM provider - it's agnostic to the project (some changes will be required).

Experiment with different projects and files:
- Make sure to run the MCP server first, then the client.
- Run the client.

✨ Features

What is MCP?

Fundamentally, MCP is a standardized method that enables applications to provide their environment, user preferences, and conversation history to AI models in a more comprehensive way. Think of it as a means to offer intelligent, structured context feeds to AI systems.

MCP Structure

Problems Solved by MCP

Current AI systems have limited "working memory" - they can only view a specific amount of conversation history at a time (their "context window"). Imagine having a conversation with someone who can only remember the most recent exchanges:

You: "Remember that project we discussed last week about optimizing the supply chain?"
AI without good context: "I don't recall that specific discussion. Can you remind me of the details?"

This limitation forces users to repeatedly explain content, resulting in a frustrating interaction experience. MCP addresses this issue by creating a structured method for maintaining and accessing context.

Analogy

1. GPS Navigation

Traditional AI context management is like someone who only gives one turn direction at a time without showing the full map. If they forget a step, the entire conversation breaks down.

MCP is like a GPS navigation system: