MCP Knowledge Base
@gmogmzGithub
About MCP Knowledge Base
A lightweight knowledge base assistant using MCP with LLM integration. Features a streamlined server-client architecture combining custom tools with a knowledge base, all accessible via SSE transport. Ideal for building simple AI-powered knowledge assistants.
Config
No standard config provided
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RepositoryTools
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Overview
What is MCP Knowledge Base?
A simple MCP client‑server application that uses OpenAI to answer questions from a built‑in knowledge base. It is intended for learning the MCP protocol or building a custom Q&A system.
How to use MCP Knowledge Base?
Install dependencies with Poetry, set your OpenAI API key in a .env file, start the server via poetry run python server.py, and run the client with poetry run python client-sse.py. The client offers two modes: direct tool calls (uncomment the test line) or LLM‑powered interactions that interpret natural‑language queries.
Key features of MCP Knowledge Base
- MCP server exposing tools via decorators
- MCP client with LLM query interpretation
- Knowledge base stored in
data/kb.json - Direct tool‑call mode for testing
- LLM‑powered mode for natural‑language questions
Use cases of MCP Knowledge Base
- Learn the MCP client‑server architecture interactively
- Build a domain‑specific Q&A system with a custom knowledge base
- Prototype tool calls with or without an LLM orchestrator
FAQ from MCP Knowledge Base
What are the runtime requirements?
Python 3.9 or higher, Poetry for dependency management, and a valid OpenAI API key.
How do I change the knowledge base content?
Edit the data/kb.json file with the Q&A pairs you want to use.
How do I add a new server tool?
Define a new Python function decorated with @mcp.tool() inside server.py.
How do I run the client without an LLM?
Uncomment the asyncio.run(test_direct_tool_calls()) line in client-sse.py to call tools directly.
How do I change the LLM model used?
Modify the model parameter in the MCPClient initialization inside client-sse.py.
Frequently asked questions
What are the runtime requirements?
Python 3.9 or higher, Poetry for dependency management, and a valid OpenAI API key.
How do I change the knowledge base content?
Edit the `data/kb.json` file with the Q&A pairs you want to use.
How do I add a new server tool?
Define a new Python function decorated with `@mcp.tool()` inside `server.py`.
How do I run the client without an LLM?
Uncomment the `asyncio.run(test_direct_tool_calls())` line in `client-sse.py` to call tools directly.
How do I change the LLM model used?
Modify the `model` parameter in the `MCPClient` initialization inside `client-sse.py`.
Basic information
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