Overview

The MCP Documentation Server is a lightweight, TypeScript‑based Model Context Protocol (MCP) service that turns any local collection of documents into an AI‑ready knowledge base. By combining fast on‑disk persistence, an in‑memory keyword index, and optional Google Gemini AI integration, it solves the common pain point of “searching a file‑based knowledge base” without requiring a full database stack. Developers can simply drop PDFs, Markdown files or plain text into the uploads folder and immediately expose them to any MCP‑compatible client such as Claude Desktop or the DeepWiki workflow.

At its core, the server offers three complementary search modalities. First, a traditional semantic search that chunks documents and scores each chunk with embeddings from a configurable model; this delivers quick, relevance‑based results for keyword or phrase queries. Second, an AI‑powered search layer that forwards the query to Gemini, which understands context, relationships and higher‑level concepts across documents. Finally, a context window retrieval tool that returns neighboring chunks around a hit, enabling downstream LLMs to generate richer answers without having to re‑search the entire corpus. These capabilities are exposed through a small, well‑typed set of MCP tools (, , , etc.) that developers can invoke directly from their assistant.

Performance is a hallmark of the design. An O(1) allows instant lookup by ID, while an LRU prevents expensive recomputation of vector embeddings. Parallel chunking and streaming file readers enable ingestion of large PDFs without exhausting memory, and the copy‑based storage keeps a pristine backup of each original file. All data lives under , eliminating external database dependencies and making the server truly local‑first.

Real‑world use cases abound: a technical writer can query their own style guide for consistent terminology; a product manager can ask an assistant to summarize the latest release notes; a researcher can retrieve contextual insights across multiple conference papers. Because the server integrates natively with MCP, any LLM client that supports the protocol can tap into these tools, turning static documents into dynamic, AI‑powered knowledge sources. The optional Gemini integration adds a layer of semantic depth that goes beyond keyword matching, making the server especially valuable in domains where understanding nuance and relationships is critical.