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.
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
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: kbdb
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
Tool Schema Changelog
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This server cannot be deployed
Maintenance
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