closed-book-mcp

Zero-Hallucination, Closed-Book Retrieval Engine via Model Context Protocol (MCP)

A lightning-fast, local Retrieval-Augmented Generation (RAG) system built on the Model Context Protocol (MCP). ClosedBook enforces authoritative, ground-truth retrieval over your local documents (PDFs, manuals, SOPs, study materials) using Hybrid Search (Dense Semantic + BM25Okapi)—completely cutting out hallucinations and overriding unverified model assumptions.

Features

• Hybrid Search Architecture: Combines BAAI/bge-small-en-v1.5 (semantic understanding) with BM25Okapi (exact keyword matching) to catch tricky legal or technical phrasing.

• Strict Closed-Book Mode: Forces the AI to rely entirely on the provided PDFs, ignoring conflicting pretrained knowledge (perfect for exams where the textbook has specific/outdated answers).

• 100% Local Privacy: Runs embedding and vector searches locally on your CPU using faiss-cpu. No cloud API required.

• Agent Integration: Plugs directly into the Antigravity AI agent framework or any MCP-compatible client.

Related MCP server: PDF RAG MCP Server

Prerequisites

Before you begin, ensure you have met the following requirements:

• You have installed Python 3.12+.

• You have installed uv (an extremely fast Python package installer and resolver).

Getting Started

1. Install Dependencies

Clone the repository, navigate to the folder, and run:

uv sync

2. Add Your Study Materials

Place your exam study materials (PDFs) into the resources/ directory.

mkdir -p resources
# copy your PDFs here (e.g., cp ~/Downloads/lecture_slides.pdf resources/)

3. Build the Search Index

Run the ingestion script to chunk your PDFs and generate the vector embeddings.

uv run ingest.py

4. Configure Antigravity (or your MCP Client)

Open your Antigravity MCP configuration file (typically ~/.gemini/config/mcp_config.json) and add this server block. Make sure to update the absolute path to point to your cloned repository:

{
  "mcpServers": {
    "mcp_closed_book": {
      "command": "uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/ExamMCP",
        "run",
        "server.py"
      ]
    }
  }
}

Usage

Once the MCP server is configured and your AI Agent is restarted, it will automatically detect the rules in GEMINI.md.

Simply batch paste your multiple-choice questions into the chat:

1. According to the act, no suit or prosecution shall lie against whom?
A) The contractor
B) Any person acting in good faith
C) The court
D) The victim

The AI will run parallel background searches against your PDF index and immediately output the correct option based only on the text.

Customizing

If you are forking this repo for your own exams, here is what you need to change:

1. The PDFs (resources/): Delete the existing PDFs and replace them with your own study guides, lecture slides, or textbooks.

2. Chunking Size (ingest.py): By default, the system uses 1000-character chunks with a 200-character overlap. If your exam requires extreme precision (like math formulas), you may want to decrease chunk size.

3. The System Prompt (GEMINI.md): The root GEMINI.md file contains the strict rules for the AI. If your exam requires essay-style answers instead of Multiple Choice (MCQ), edit GEMINI.md and change the instructions to output detailed paragraphs instead of just "Option A".

4. Rebuild the Index: Whenever you change your PDFs, you must run uv run ingest.py again!

Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

License

This project is licensed under the MIT License - see the LICENSE file for details.