Antigravity PDF MCP Server

A powerful Model Context Protocol (MCP) server that enables intelligent document ingestion and querying capabilities for AI agents and IDEs. This server allows you to build a persistent knowledge base from PDFs, Markdown, and Text files, and query them using advanced hybrid search techniques.

Features

• Multi-Format Ingestion: Support for .pdf, .md, and .txt files.

• Smart Chunking: Recursive character splitting preserves document structure (paragraphs, headers).

• Persistent Storage: Uses SQLite (antigravity.db) to store documents and vectors across restarts.

• Advanced Retrieval:

  • Hybrid Search: Combines TF-IDF (keyword) and OpenAI Embeddings (semantic) using Reciprocal Rank Fusion (RRF).

  • Filtering: Scope searches to specific documents.

  • Citations: Returns page numbers (e.g., [Page 5]) for easy verification.

• User Experience: Real-time progress notifications during ingestion.

• MCP Protocol: Fully compliant with the Model Context Protocol over Stdio.

Prerequisites

• Node.js (v18 or higher)

• npm

Installation

1. Clone the repository:

   git clone <repository-url>
   cd antigravity-pdf-mcp

2. Install dependencies:

   npm install

3. Build the project:

   npm run build

Configuration

To enable Semantic Search (Embeddings), create a .env file in the root directory:

OPENAI_API_KEY=sk-your-api-key-here

If no API key is provided, the server will fallback to local TF-IDF search only.

Tools

The server exposes the following MCP tools:

• ingest_document: Ingest a file (PDF, TXT, MD) into the knowledge base.

  • path: Absolute path to the file.

• query_knowledge_base: Search the knowledge base.

  • query: The search query.

  • document_id (Optional): Filter results to a specific document ID.

• list_documents: List all ingested documents.

• reset_library: Clear the entire database.

• ingest_pdf (Deprecated): Alias for ingest_document.

Usage with IDEs

This server uses the Stdio transport, making it compatible with any MCP-compliant client or IDE.

Antigravity IDE

1. Open Settings > MCP Servers.

2. Click Add Server.

3. Configure the server:

   • Name: antigravity-pdf

   • Command: node

   • Arguments:

     /absolute/path/to/antigravity-pdf-mcp/dist/server.js

   • Environment Variables:

     • OPENAI_API_KEY: Your OpenAI API key.

VSCode / Claude Desktop

Add to your MCP configuration file (e.g., claude_desktop_config.json):

{
  "mcpServers": {
    "antigravity-pdf": {
      "command": "node",
      "args": [
        "/absolute/path/to/antigravity-pdf-mcp/dist/server.js"
      ],
      "env": {
        "OPENAI_API_KEY": "your-api-key-if-needed"
      }
    }
  }
}

Contributing

1. Fork & Clone: Clone your fork locally.

2. Branch: Create a feature branch (git checkout -b feature/amazing-feature).

3. Develop: Make your changes.

4. Verify:

   • Run npm run build to check for errors.

   • Use npx ts-node verify_ux.ts to test ingestion and retrieval.

5. Commit & Push: Push changes to your fork.

6. Pull Request: Open a PR against the main repository.

License

MIT