MCP Knowledge Base Assistant

Created By
kofiadoma year ago
An intro to MCP: an MCP server with a knowledge base tool containerized with Docker and connected to a client-side python application using SSE
Overview

What is MCP Knowledge Base Assistant?

MCP Knowledge Base Assistant is a project that demonstrates the Model Context Protocol (MCP) by connecting an OpenAI-powered client to a knowledge base server. It allows users to ask questions about company policies and retrieves accurate answers from a structured knowledge base.

How to use MCP Knowledge Base Assistant?

To use the MCP Knowledge Base Assistant, clone the repository, set up a Python environment, and run the server and client applications. Users can ask questions in natural language, and the AI will respond with relevant information from the knowledge base.

Key features of MCP Knowledge Base Assistant?

  • Integration with OpenAI's API for natural language processing
  • Containerization of the server using Docker
  • Ability to query a knowledge base for company policy information
  • Simple architecture with client-server communication

Use cases of MCP Knowledge Base Assistant?

  1. Answering employee questions about company policies.
  2. Providing quick access to information for HR departments.
  3. Enhancing internal knowledge sharing within organizations.

FAQ from MCP Knowledge Base Assistant?

  • Can I customize the knowledge base?

Yes! You can add more question-answer pairs to the data/kb.json file.

  • Is Docker required to run the server?

No, Docker is optional; you can run the server directly with Python.

  • How does the client connect to the server?

The client connects to the server using Server-Sent Events (SSE) for real-time communication.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
kofiadom
Star
0
Language
Python
License
-

Recommend Servers

View All
Docwand

14 hours ago
//beforeyouship — LLM Cost Modeling From Your Editor
@Indiegoing

Query realistic LLM cost models without leaving your editor. beforeyouship models the **true monthly cost** of an LLM app architecture — retries, prompt caching, batch discounts, infra overhead, and 3×/10× growth — across GPT-5.x, Claude, Gemini, DeepSeek, and more. Not a token calculator: a planning tool for the design phase, before you commit to a stack. **No API key needed to try it** — demo mode covers the six free-tier models. A Pro key from [beforeyouship.dev](https://beforeyouship.dev) unlocks the full 18-model catalog. ## What you can ask - "How much will a RAG chatbot cost at 10,000 requests/day?" - "Compare Claude Haiku vs Gemini Flash pricing for my workload" - "What's the cheapest model for a multi-step agent at scale?" - "Show me current per-token prices for Anthropic models" ## Tools ### `estimate_cost` Full cost model for an architecture at a given usage level. Returns Naive / Realistic / Worst Case monthly cost per model, 3×/10× growth scenarios, and an opinionated recommendation with reasoning. ### `get_model_prices` Current per-1M-token pricing — input, output, cached input, batch — with context windows and staleness metadata. ### `list_archetypes` Seven preset architecture patterns (simple chatbot, chatbot with history, RAG pipeline, multi-model router, coding assistant, document processor, multi-step agent) used as starting points for estimates. ## Setup **Claude Code:** ​```bash claude mcp add --transport http beforeyouship https://beforeyouship.dev/api/mcp ​``` **Cursor / other clients** — add a remote server: ​```json { "mcpServers": { "beforeyouship": { "type": "streamable-http", "url": "https://beforeyouship.dev/api/mcp" } } } ​``` Add an `Authorization: Bearer bys_...` header with a Pro key for the full catalog. ## Try it > Estimate the monthly cost of a RAG pipeline at 10,000 requests/day

14 hours ago