🧠 Knowledge Assistant MCP

📌 Overview

Finding the right information from a massive collection of documents is often slow and manual. Existing Large Language Models (LLMs) are excellent at generating and summarizing text, but out-of-the-box, they cannot directly access local documents or perform structured operations on them. As the number of documents grows, manually copying, pasting, and providing context to an LLM becomes highly inefficient and error-prone.

The Goal: Build an MCP (Model Context Protocol) server that exposes a suite of powerful document analysis capabilities as reusable tools. Instead of embedding all the logic into complex prompts, an AI assistant can seamlessly invoke these specialized tools to autonomously locate, extract, summarize, compare, and analyze documents stored locally on your machine.

Related MCP server: directory-indexer

🎯 Objectives

• Create a reusable MCP server dedicated to local document processing.

• Enable AI assistants (like Copilot or Claude) to interact with local documents through structured, predefined tool calls.

• Eliminate manual effort by reducing the need to manually search, open, read, and copy-paste from multiple files.

• Ensure structured outputs instead of relying solely on unpredictable, free-form AI responses for precise data extraction.

🔎 Scope

This MCP operates entirely on local files, ensuring data privacy, offline capability, and high speed.

Supported Document Types:

• 📄 PDF (.pdf)

• 📝 Markdown (.md)

• 📜 Plain Text (.txt)

Core Capabilities (Tools Provided):

1. Discover: List and identify available documents in a target directory.

2. Read: Ingest and parse document contents.

3. Extract Text: Pull specific text blocks or metadata from files.

4. Summarize: Generate concise, accurate summaries of lengthy documents.

5. Compare: Cross-reference and evaluate two different documents (e.g., matching a resume to a job description).

6. Keyword Extraction: Identify and extract critical keywords, themes, or topics.

7. Search: Locate specific documents containing targeted terms, phrases, or semantic meaning.

8. Analyze: Generate structured insights and tabular reports from document data.

🛠️ Example Workflow

Imagine you have a local project folder structured like this:

📁 documents/
├── 📄 Resume.pdf
├── 📄 AI_Engineer_JD.pdf
├── 📄 Product_Requirements.pdf
├── 📄 API_Documentation.pdf
└── 📝 Meeting_Notes.md

Instead of manually opening and reading each document, a user can simply ask their AI assistant:

"Summarize the Product Requirements document."

"Compare my resume with the AI Engineer job description."

"Which documents mention OAuth authentication?"

Behind the scenes: The AI assistant invokes one or more of the MCP tools you built to dynamically retrieve the exact required information, process it, and produce a synthesized final answer for the user.

🤔 Why MCP?

LLMs cannot autonomously discover, navigate, or operate over your local filesystem unless they are given explicit, safe tools to do so.

❌ Without MCP (The Manual Way)

  [ User ] 
     │  (Manually finds, opens, copies text, and pastes into chat)
     ▼
   [ LLM ]

✅ With MCP (The Automated Way)

  [ User ] ── "Compare my resume to the JD"
     │
     ▼
   [ LLM ] ── Decides which tools to call
     │
     ▼
[ Document MCP Server ]
     ├── 🔍 Search Documents
     ├── 📖 Read Document
     ├── ⚖️ Compare Documents
     ├── 🔑 Extract Keywords
     └── 📝 Summarize

The LLM acts as the orchestrator, while the MCP server acts as the capable hands executing the local file operations.