Knowledge Base MCP Server

Overview

The Jeanibarz Knowledge Base MCP Server bridges the gap between static document repositories and AI assistants by exposing a lightweight, vector‑searchable knowledge base through the Model Context Protocol. Instead of hard‑coding facts into prompts, developers can now host a directory of text files—such as corporate policy documents, support manuals, or onboarding guides—and let the server automatically generate and update embeddings that an assistant can query on demand. This approach keeps knowledge fresh, scales with file size, and eliminates manual prompt engineering.

What Problem Does It Solve?

Modern AI assistants often struggle with up‑to‑date, domain‑specific information. Embedding every possible fact into a prompt is infeasible for large corpora, and manually updating prompts is error‑prone. The MCP server solves this by providing an on‑demand retrieval layer: when a user asks a question, the assistant sends a query to the server, which returns the most relevant text chunks from the indexed knowledge bases. This reduces hallucination and ensures that responses are grounded in authoritative sources.

Core Functionality & Value

• Automatic indexing: The server monitors a configurable root directory, recurses through subfolders, and processes all and files. It splits content into manageable chunks with a Markdown‑aware splitter, hashes each file to detect changes, and stores embeddings in a FAISS vector index.
• Dynamic updates: Whenever files are added, removed, or modified, the server re‑indexes only the affected parts, keeping the search index current without full rebuilds.
• MCP integration: By exposing tools that list available knowledge bases and retrieve matching chunks, the server fits seamlessly into any MCP‑enabled workflow. AI assistants can call these tools without needing direct file system access or custom API endpoints.
• Model flexibility: The server supports any Hugging Face embedding model via environment variables, allowing developers to trade accuracy for speed or cost.

Use Cases & Real‑World Scenarios

• Enterprise support: A helpdesk assistant can pull from an knowledge base, answering configuration questions or troubleshooting steps with precise excerpts.
• Onboarding: New hires receive context‑rich explanations from an base, including policy clauses and procedural steps.
• Regulatory compliance: Legal assistants can query a base to ensure policy‑conformant responses.
• Developer documentation: Technical assistants can fetch code snippets or API usage notes from a base, reducing reliance on external documentation sites.

Integration into AI Workflows

Developers integrate the server by adding its MCP configuration to their client settings. Once registered, an assistant can invoke a tool to discover available knowledge bases and a tool to retrieve relevant chunks. The assistant can then embed these snippets into the context window, ensuring that its output is anchored in verified content. Because the server operates over MCP, it remains agnostic to the assistant’s underlying language model, making it a drop‑in component for any Claude or similar AI environment.

Unique Advantages

• Zero code in prompts: All knowledge retrieval logic lives on the server, freeing prompt designers from embedding heavy logic.
• Scalable vector search: FAISS provides sub‑millisecond similarity queries even for large corpora, enabling real‑time assistance.
• Fine‑grained control: By organizing knowledge into subdirectories, teams can enforce access policies and modularize content.
• Cost‑effective: Leveraging free Hugging Face models (e.g., ) keeps inference costs low while maintaining high retrieval quality.

In sum, the Jeanibarz Knowledge Base MCP Server transforms static documentation into an interactive, AI‑ready resource, empowering assistants to deliver accurate, contextually relevant answers across a wide range of professional domains.