What are the runtime requirements?
Python 3.9 or higher, Poetry for dependency management, and a valid OpenAI API key.
@gmogmzGithub
Basic information
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.
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryTool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
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.
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.
data/kb.jsonPython 3.9 or higher, Poetry for dependency management, and a valid OpenAI API key.
Edit the `data/kb.json` file with the Q&A pairs you want to use.
Define a new Python function decorated with `@mcp.tool()` inside `server.py`.
Uncomment the `asyncio.run(test_direct_tool_calls())` line in `client-sse.py` to call tools directly.
Modify the `model` parameter in the `MCPClient` initialization inside `client-sse.py`.
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