MCP Knowledge Base

What is MCP Knowledge Base?

MCP Knowledge Base is a personal and public repository that explains the Model Context Protocol (MCP) — an emerging standard for enabling LLMs to call tools via structured interactions. It is intended for learners and explorers of MCP, providing explanations, diagrams, sample Python code, and notes.

How to use MCP Knowledge Base?

Browse the repository and explore the examples/, notes/, diagrams/, and schemas/ folders. The README describes planned sample scripts in examples/ that simulate MCP host-server communication and tool invocation.

Key features of MCP Knowledge Base

• Explanations of MCP communication flow
• Diagrams showing interactions between host, server, LLM, and tools
• Sample Python code simulating the MCP pipeline
• Notes and references to papers or discussions
• Exploration of toolchains, caching, and state management in MCP

Use cases of MCP Knowledge Base

• Understand how the Model Context Protocol works from user input to final output
• Study tool discovery, execution, and result handling in an MCP setup
• Reference a structured summary of MCP concepts (tools, host, server, schemas, context handling)
• Get started with minimal Python examples of an MCP pipeline

FAQ from MCP Knowledge Base

What is the Model Context Protocol (MCP)?

MCP is an emerging standard that enables LLMs to call tools via structured interactions between a host, server, and LLM.

How does MCP work in this knowledge base?

The lifecycle includes user input, tool discovery, query dispatch, tool selection by the LLM, tool execution via the server, result handling, and final output.

Are there working code examples in this repository?

The README states that examples like basic_mcp_demo.py, tool_plugin_example.py, and mcp_host_server_flow.py will be added to the examples/ folder.

What dependencies or runtime are needed?

The README does not specify any required dependencies or runtime environments.

Where is the official MCP specification referenced?

References to LangChain's tool calling, OpenAI's Function Calling, and future MCP spec links are mentioned; specific spec links are to be added.