Which integrations are available for this server?

Provides tools for querying PDF documents using NVIDIA's AI models for embeddings and text generation.

How do I use MCP PDF Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP PDF Server What does the report say about revenue growth?" 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.

MCP PDF Server

by BhagyeshRashinkar

Overview Schema Source Related Servers Discussions Admin

TypeScript

Hybrid

📚 MCP PDF Server

An MCP (Model Context Protocol) server that lets AI assistants query your PDF documents. Drop your PDFs, ingest them into a vector database, and ask questions — answers are grounded in your actual documents.

✨ Features

🔌 MCP-Compatible — Works with any MCP client (GitHub Copilot, Antigravity, etc.)
📄 Auto PDF Discovery — Automatically finds, extracts, chunks, and embeds all PDFs in your folder
🔍 Vector Search — Retrieves the most relevant passages before generating answers
🐳 Docker-Ready — Runs as a containerized server with one command
🗄️ Qdrant — Fast, open-source vector database for similarity search

Related MCP server: PDF RAG MCP Server

🏗️ How It Works

┌─────────────┐     MCP (stdio)     ┌───────────────────┐     HTTP      ┌──────────┐
│ AI Assistant │◄───────────────────►│  MCP PDF Server   │◄────────────►│  Qdrant  │
│              │                     │                   │              │ Vector DB│
└─────────────┘                     │  1. Embed question │              └──────────┘
                                    │  2. Search vectors │
                                    │  3. Generate answer│   LLM API
                                    │                    │◄────────────►
                                    └───────────────────┘   (Embeddings
                                                            + Generation)

You ask a question via your AI assistant.
The server embeds the question using your choice of embedding model.
It searches Qdrant for the top 5 most relevant text chunks from your PDFs.
It generates an answer using an LLM, grounded in the retrieved context.

🚀 Quick Start

Prerequisites

Docker & Docker Compose
Node.js 20+ (for ingestion only)
[LLM API Key]

1. Clone & Configure

git clone https://github.com/your-username/mcp-pdf-server.git
cd mcp-pdf-server

cp .env.example .env

Edit .env and set your API key:

API_KEY=nvapi-your_key_here

2. Start Qdrant

docker-compose up -d

3. Add Your PDFs & Ingest

Place your PDF documents in the pdfs/ folder, then:

npm install        # first time only
npm run ingest

All PDFs in the folder are automatically discovered and ingested.

4. Build the Server Image

docker build -t mcp-pdf-server .

5. Connect to Your AI Assistant

Add to your AI assistant's MCP config (e.g., mcp_config.json):

{
  "mcpServers": {
    "pdf-docs": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "--network",
        "mcp-network",
        "-e",
        "API_KEY",
        "-e",
        "QDRANT_URL=http://mcp-qdrant:6333",
        "-e",
        "COLLECTION_NAME=documents",
        "-e",
        "EMBED_MODEL=nvidia/nv-embedqa-e5-v5",
        "-e",
        "GEN_MODEL=qwen/qwen2.5-coder-32b-instruct",
        "mcp-pdf-server"
      ],
      "env": {
        "API_KEY": "your_nvapi_key_here"
      }
    }
  }
}

Done! Ask your AI assistant any question about your documents.

🔧 Available Tools

Tool	Description
`ask_documents`	Ask any question. The server retrieves relevant context from your ingested PDFs and generates an answer.

⚙️ Environment Variables

Variable	Description	Default
`API_KEY`	LLM API key	(required)
`EMBED_MODEL`	Embedding model	`nvidia/nv-embedqa-e5-v5`
`GEN_MODEL`	Generation model	`qwen/qwen2.5-coder-32b-instruct`
`COLLECTION_NAME`	Qdrant collection name	`documents`
`QDRANT_URL`	Qdrant connection URL	`http://localhost:6333`
`EMBED_BATCH_SIZE`	Chunks per embedding batch	`15`
`EMBED_MAX_RETRIES`	Max retries on API failure	`3`
`EMBED_COOLOFF_MS`	Cooldown between batches (ms)	`500`

Note: The .env file is used for local ingestion. The mcp_config.json passes env vars via Docker -e flags for the server.

📁 Project Structure

mcp-pdf-server/
├── pdfs/                     # Place your PDF documents here
├── src/
│   ├── server.ts             # MCP server entry point
│   ├── llm/
│   │   └── provider.ts       # LLM API client (embed + generate)
│   ├── vector/
│   │   └── qdrant.ts         # Qdrant client config
│   └── ingest/
│       ├── main.ts           # Ingestion orchestrator (auto-discovers PDFs)
│       ├── extract.ts        # PDF text extraction
│       ├── chunk.ts          # Text chunking
│       └── embed.ts          # Batch embedding & Qdrant insertion
├── docker-compose.yml        # Qdrant service
├── Dockerfile                # Server image
├── .env.example              # Env var template (safe to commit)
├── .gitignore                # Keeps secrets & binaries out of git
└── package.json

🛠️ Development

For local development with hot-reloading:

npm install
docker-compose up -d    # Start Qdrant
npm run dev             # Server with hot-reload

To use the local dev server with your AI assistant, change mcp_config.json to:

{
  "mcpServers": {
    "pdf-docs": {
      "command": "npx",
      "args": ["tsx", "src/server.ts"],
      "cwd": "/path/to/mcp-pdf-server",
      "env": {
        "API_KEY": "your_nvapi_key_here",
        "QDRANT_URL": "http://localhost:6333",
        "COLLECTION_NAME": "documents",
        "EMBED_MODEL": "nvidia/nv-embedqa-e5-v5",
        "GEN_MODEL": "qwen/qwen2.5-coder-32b-instruct"
      }
    }
  }
}

📝 Use Cases

This server works with any PDF knowledge base:

📖 Technical books — Architecture, algorithms, system design
📋 Company docs — Wikis, runbooks, policies
📄 Research papers — Academic papers, whitepapers
📑 Legal documents — Contracts, compliance
🎓 Course material — Textbooks, lecture notes

🐛 Troubleshooting

Problem	Solution
Server can't reach Qdrant	`docker ps` — Ensure `mcp-qdrant` is running on `mcp-network`
Embeddings mismatch	Changed `EMBED_MODEL`? Delete `qdrant_storage/` and re-ingest
Rebuild server image	`docker build -t mcp-pdf-server .` after code changes
Reset all data	Delete `./qdrant_storage/` and re-run `npm run ingest`
Rate limiting	Increase `EMBED_COOLOFF_MS` or decrease `EMBED_BATCH_SIZE` in `.env`
No PDFs found	Ensure `.pdf` files are placed in the `pdfs/` directory

📄 License

ISC

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