MCP Knowledge Base

This project is a personal and public knowledge base about the Model Context Protocol (MCP), aiming to explain and explore the working principles, interaction processes, and tool call mechanisms of MCP, including example code, concept analysis, and reference materials.

Developer tools Artificial intelligence chatbots #Tool invocation #LLM interaction #Protocol analysis #Knowledge base

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update time : 2025-04-29

What is the MCP server?

The MCP server is the core component of the Model Context Protocol, serving as a bridge between LLMs and external tools (such as APIs and databases). It is responsible for tool discovery, call coordination, and result return, enabling AI to dynamically use external services.

How to use the MCP server?

Send requests through an MCP host (such as a VS Code plugin). The MCP server will automatically handle the tool registration, discovery, and call processes. Users only need to interact with the natural language interface.

Applicable scenarios

Suitable for scenarios where AI needs to call external services, such as real - time data queries, computational tasks, and workflow automation.

Main features

Tool discoveryAutomatically maintain a catalog of available tools, supporting dynamic registration and updates

Context managementMaintain the conversation history and context for multi - step tool calls

Secure invocationProvide authentication and authorization mechanisms for tool calls

Advantages and limitations

Advantages

Seamlessly expand AI capabilities without modifying the core model

Support multiple tool types and protocols

Reduce the difficulty of developing complex AI applications

Limitations

Depend on the interface stability of tool providers

Complex tool chains may increase latency

Require clear tool descriptions to ensure correct use

How to use

Start the MCP server

Run the server program and ensure that the tool endpoints are accessible

Connect via the host

Enter natural language requests in an MCP - supported client (such as an IDE plugin)

Usage examples

Weather queryQuery real - time weather information through natural language

Mathematical calculationPerform complex mathematical operations

Frequently asked questions

Do I need to deploy the MCP server myself?

How to ensure the security of tool calls?

Does it support custom tool development?

Related resources

MCP protocol draft

Official protocol specification document

GitHub example library

Various MCP implementation examples

MCP tool development guide

How to develop MCP - compatible tools

🚀 MCP Knowledge Base

This repository is a personal and public knowledge base centered around the Model Context Protocol (MCP), a novel standard enabling large language models (LLMs) to invoke tools through structured interactions.

🚀 Quick Start

This repository is under development to deepen my understanding of the Model Context Protocol (MCP), a new standard that allows large language models (LLMs) to invoke tools through structured interactions. It may contain some early explanations or conceptual gaps, which will be gradually refined as MCP evolves.

✨ Features

Content explaining the MCP communication process.
Diagrams showing the interactions between the host, server, LLM, and tools.
Sample Python code simulating the MCP pipeline.
Notes and references to papers or discussions.
A place to explore new ideas, such as toolchains, caching, and state management in MCP.

🔧 Technical Details

🔄 How MCP Works

The following is a brief description of the MCP lifecycle based on early experiments:

💬 User Input: The user poses a question (e.g., in VS Code or a browser IDE, which acts as the MCP Host).
📡 Tool Discovery: The host sends a request to the MCP Server to discover available tools or plugins.
📤 Query Distribution: The host passes the user query and tool metadata to the LLM.
🧠 Tool Selection: The LLM selects one or more tools to fulfill the query (e.g., a weather API or a calculator).
🛠️ Tool Execution: The host executes the tool(s) by calling the MCP Server.
📥 Result Handling: The tool returns its results; the host passes them back to the LLM.
✅ Final Output: The LLM responds with a complete answer containing all the necessary context.

🧪 Examples and Simulations

In the examples/ folder:

basic_mcp_demo.py: A basic example of MCP host - server communication will be added.
tool_plugin_example.py: It will demonstrate tool registration and invocation.
mcp_host_server_flow.py: It will demonstrate the full lifecycle from user input to LLM response.

🧱 Concepts and Building Blocks

Tools: External services (APIs, databases, functions) through which LLMs can extend their capabilities.
Host: The interface environment where the LLM runs and displays results.
Server: Middleware that handles the logistics of tool invocation.
Schema: JSON schemas or data formats for structured requests and responses.
Context Handling: Ensures that the LLM can access all previous tool invocations to maintain the coherence of answers.

📚 Documentation

📁 Suggested Repository Structure

mcp-knowledge-base/
│
├── README.md
├── 图表/
│   └──                       # Any visual explanations
├── examples/
│   ├── basic_mcp_demo.py
│   └── tool_plugin_example.py
├── mcp_host_server_flow.py
└── 资料夹/

Notes:

Ensure the repository structure is clear, and use Chinese for file and directory names.
Each folder should contain relevant content, such as example code and documentation.
The README.md should detail the repository's content and usage.