Antigravity PDF MCP Server
Uses OpenAI's embedding API for semantic search capabilities within the knowledge base, enabling hybrid search that combines keyword and semantic retrieval techniques.
Provides persistent storage for ingested documents and vector embeddings, maintaining a queryable knowledge base across server restarts.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Antigravity PDF MCP Serversearch for information about smart chunking and hybrid search techniques"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.txtfiles.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.
Related MCP server: Enterprise Knowledge MCP Server
Prerequisites
Node.js (v18 or higher)
npm
Installation
Clone the repository:
git clone <repository-url> cd antigravity-pdf-mcpInstall dependencies:
npm installBuild 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-hereIf 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 foringest_document.
Usage with IDEs
This server uses the Stdio transport, making it compatible with any MCP-compliant client or IDE.
Antigravity IDE
Open Settings > MCP Servers.
Click Add Server.
Configure the server:
Name:
antigravity-pdfCommand:
nodeArguments:
/absolute/path/to/antigravity-pdf-mcp/dist/server.jsEnvironment 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
Fork & Clone: Clone your fork locally.
Branch: Create a feature branch (
git checkout -b feature/amazing-feature).Develop: Make your changes.
Verify:
Run
npm run buildto check for errors.Use
npx ts-node verify_ux.tsto test ingestion and retrieval.
Commit & Push: Push changes to your fork.
Pull Request: Open a PR against the main repository.
License
This server cannot be deployed
Maintenance
Related MCP Connectors
Ingest, manage, and retrieve documents for RAG-powered AI applications
Search and reason over your Obsidian-style Markdown vault, right from ChatGPT.
Your private knowledge base: upload documents (.md, .txt, .docx, PDF, images), the platform indexes
Search your knowledge bases from any AI assistant using hybrid RAG.
Related MCP Servers
- FlicenseNot gradedqualityDmaintenanceEnables RAG (Retrieval-Augmented Generation) capabilities with document processing, vector storage, and intelligent Q\&A using OpenAI embeddings and semantic search.-
- AlicenseNot gradedqualityDmaintenanceEnables querying enterprise documents (DOCX, PDF, PPTX) using natural language, with hybrid search and MCP integration for Claude Desktop and other agents.MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to perform semantic, hybrid, and filtered search on indexed local documentation with RAG capabilities.2MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to parse and search documents including PDF, Word, Excel, PowerPoint, and images via OCR, with support for semantic search and batch processing.2MIT