PDF Indexer MCP Server

A Model Context Protocol (MCP) server that enables AI agents to download, index, and semantically search PDF research papers. This server provides 8 tools that AI agents can discover and use autonomously to build research paper knowledge bases and answer questions.

What is MCP?

Model Context Protocol (MCP) is a standardized protocol that allows AI agents to discover and use tools. Instead of being limited to text generation, agents become action-capable systems that can:

1. Discover Tools: Agents automatically discover available tools from connected MCP servers

2. Understand Capabilities: Agents read tool descriptions and parameters to understand what each tool can do

3. Execute Tasks: Agents call tools with appropriate parameters to accomplish goals

4. Compose Workflows: Agents can combine multiple tools from different servers to solve complex problems

How MCP Works

AI Agent → MCP Protocol → Tool Server → Execution → Results → Agent

When you connect this MCP server to an AI agent (like in Cursor, Claude Desktop, or via OpenAI Agents framework), the agent automatically:

• Discovers all 8 tools available from this server

• Understands what each tool does from their descriptions

• Uses the tools when they're needed to complete tasks

• Can combine tools in complex workflows

Features

• 📥 PDF Download: Download research papers from URLs

• 📄 Intelligent Chunking: Two chunking strategies:

  • Header-based: Preserves document structure (ideal for academic papers)

  • S2 chunking: Spatial-semantic hybrid approach for optimal semantic chunks

• 🗄️ Database Indexing: Store papers and chunks in SQLite with navigation indices

• 🔍 Semantic Search: Search papers using MLX-optimized embeddings (Qwen3-Embedding-0.6B)

• ⚡ FAISS Vector Index: Fast similarity search even for thousands of chunks

• 🧠 Context-Aware: Retrieve surrounding chunks for better context

Quick Start: Suggested Prompt

Once the MCP server is configured, you can use prompts like:

"I have this research paper URL: [URL]. Please download it, index it, 
make it searchable, and then search for information about [topic]."

Or more simply:

"Download and index this paper: [URL], then search it for information about [topic]."

The agent will automatically:

1. Download the PDF

2. Index it into the database

3. Generate embeddings for semantic search

4. Search for relevant content

5. Present the results

Installation

Option 1: Install from GitHub (Recommended)

# Clone the repository
git clone https://github.com/lizTheDeveloper/pdf-indexer-mcp.git
cd pdf-indexer-mcp

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Option 2: Install via pip (After Publishing)

pip install pdf-indexer-mcp

MCP Server Setup

For Cursor IDE

1. Locate MCP configuration file:

   • macOS: ~/Library/Application Support/Cursor/User/globalStorage/mcp.json

   • Windows: %APPDATA%\Cursor\User\globalStorage\mcp.json

   • Linux: ~/.config/Cursor/User/globalStorage/mcp.json

2. Add configuration (create file if it doesn't exist):

   {
     "mcpServers": {
       "pdf-indexer": {
         "command": "/absolute/path/to/pdf_indexer_mcp/venv/bin/python3",
         "args": [
           "/absolute/path/to/pdf_indexer_mcp/semantic_chunked_pdf_rag.py"
         ],
         "env": {}
       }
     }
   }

3. Restart Cursor completely (not just reload)

4. Verify: After restart, you should see 8 tools available:

   • mcp_pdf-indexer_download_pdf

   • mcp_pdf-indexer_chunk_pdf

   • mcp_pdf-indexer_index_pdf

   • mcp_pdf-indexer_list_indexed_papers

   • mcp_pdf-indexer_get_document_structure

   • mcp_pdf-indexer_get_document_section

   • mcp_pdf-indexer_generate_embeddings

   • mcp_pdf-indexer_search_research_papers

For Claude Desktop

1. Locate MCP configuration file:

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

   • Windows: %APPDATA%\Claude\claude_desktop_config.json

   • Linux: ~/.config/Claude/claude_desktop_config.json

2. Add the same configuration as above

3. Restart Claude Desktop completely

For OpenAI Agents Framework

from agents import Agent, Runner
from agents.mcp import MCPServerStdio

async with MCPServerStdio(
    name="PDF Indexer",
    params={
        "command": "/path/to/pdf_indexer_mcp/venv/bin/python3",
        "args": ["/path/to/pdf_indexer_mcp/semantic_chunked_pdf_rag.py"],
    },
) as pdf_indexer_server:
    agent = Agent(
        name="Research Assistant",
        instructions="Help users search and analyze research papers",
        mcp_servers=[pdf_indexer_server],
        model="gpt-4"
    )
    
    result = await Runner.run(
        agent,
        "Download and index this paper: https://arxiv.org/pdf/1706.03762.pdf"
    )

Available MCP Tools

This server exposes 8 tools that AI agents can use. Tools are automatically discovered by agents when the server is connected.

1. download_pdf(url: str)

What it does: Downloads a PDF research paper from a URL and saves it locally.

When agents use it: When you ask to download a paper, the agent automatically discovers and uses this tool.

Example agent workflow:

User: "Download the attention paper from arxiv"
Agent: 
  1. Discovers download_pdf tool
  2. Calls: download_pdf("https://arxiv.org/pdf/1706.03762.pdf")
  3. Returns: Downloaded paper saved as "1706.03762.pdf"

Returns:

• success: bool

• filename: str (e.g., "1706.03762.pdf")

• filepath: str (absolute path)

• message: str

2. chunk_pdf(filename: str, method: str = "header")

What it does: Extracts text from a PDF and chunks it using header-based or S2 chunking.

Parameters:

• filename: PDF filename (must be in papers/ directory)

• method: "header" (default) or "s2" for spatial-semantic chunking

When agents use it: When asked to analyze or process a PDF's structure.

Returns:

• success: bool

• num_chunks: int

• chunks: list of chunk dictionaries with preview text

• method: str (chunking method used)

3. index_pdf(filename: str, url: str = "", method: str = "header")

What it does: Complete indexing workflow - downloads (if needed), chunks, and stores in database.

When agents use it: The most common tool agents use - it handles the full pipeline.

Example agent workflow:

User: "Index this paper and make it searchable"
Agent:
  1. Discovers index_pdf tool
  2. Calls: index_pdf("paper.pdf", url="https://...", method="header")
  3. Paper is now in database and searchable

Returns:

• success: bool

• paper_id: int (database ID)

• num_chunks: int

• num_sections: int (for header method)

• message: str

4. list_indexed_papers()

What it does: Lists all papers currently indexed in the database.

When agents use it: When asked "what papers do you have?" or "show me all papers".

Returns:

• success: bool

• count: int

• papers: list of paper metadata

5. get_document_structure(filename: str)

What it does: Gets the complete structure of a paper (sections, headers, chunk ranges).

When agents use it: When you ask about a paper's structure or sections.

Returns:

• success: bool

• structure: dict with paper metadata and sections list

6. get_document_section(filename: str, ...)

What it does: Retrieves a specific section of a document.

Parameters (use one of):

• chunk_index: int - Get specific chunk by index

• header_path: str - Get section by header path (e.g., "Introduction")

• page_start / page_end: int - Get chunks in page range

When agents use it: When asked "show me the Introduction section" or "get page 5-10".

Returns:

• success: bool

• paper_id: int

• num_chunks: int

• chunks: list of full chunk content

7. generate_embeddings(filename: str, model_name: str = "mlx-community/Qwen3-Embedding-0.6B")

What it does: Generates semantic embeddings for all chunks in a paper and adds them to the FAISS vector index.

When agents use it: Agents automatically use this before semantic search.

Example agent workflow:

User: "Make this paper searchable"
Agent:
  1. Calls index_pdf() - indexes the paper
  2. Calls generate_embeddings() - makes it searchable
  3. Paper is now ready for semantic search

Returns:

• success: bool

• paper_id: int

• num_embeddings: int

• embedding_dim: int (1024 for Qwen3-Embedding-0.6B)

• model_name: str

8. search_research_papers(query: str, k: int = 5, context_window: int = 1, model_name: str = "mlx-community/Qwen3-Embedding-0.6B")

What it does: Semantically searches all indexed papers using embeddings and returns the most relevant chunks.

Parameters:

• query: Search query text

• k: Number of top results (default: 5)

• context_window: Number of neighboring chunks to include (default: 1)

• model_name: Embedding model (default: Qwen3-Embedding-0.6B)

When agents use it: When asked questions like "find papers about transformers" or "search for attention mechanisms".

Example agent workflow:

User: "What papers discuss attention mechanisms?"
Agent:
  1. Discovers search_research_papers tool
  2. Calls: search_research_papers("attention mechanisms", k=5)
  3. Gets relevant chunks with context
  4. Synthesizes answer from results

Returns:

• success: bool

• query: str (original query)

• num_results: int

• results: list of result dictionaries with:

  • chunk_id, paper_id, filename, title

  • text: Full chunk text

  • header_path: Section location

  • page_start, page_end: Page numbers

  • distance: Similarity score (lower = more similar)

  • is_context: bool (true if context chunk, not direct match)

How Agents Use These Tools

Autonomous Tool Discovery

When you connect this MCP server, agents automatically discover all 8 tools. Each tool has:

• Name: What the tool is called

• Description: What the tool does (agents read this!)

• Parameters: What inputs the tool needs

• Return Type: What the tool returns

Agents use these descriptions to understand when to use each tool.

Typical Agent Workflow

User: "Find papers about transformers and summarize the key findings"

Agent workflow:
1. Discovers list_indexed_papers() → checks what's available
2. Discovers search_research_papers() → searches for "transformers"
3. Discovers get_document_section() → gets more context for top results
4. Synthesizes findings into summary

All tool calls happen autonomously!

Multi-Tool Composition

Agents can combine tools in sophisticated ways:

# Example: Agent decides to do a complete research workflow
1. download_pdf("https://arxiv.org/pdf/...") 
   → Downloads paper
2. index_pdf("paper.pdf", url="...", method="header")
   → Indexes paper in database
3. generate_embeddings("paper.pdf")
   → Makes it searchable
4. search_research_papers("related topic", k=5)
   → Finds related papers
5. get_document_section(filename, header_path="Introduction")
   → Gets specific sections for context

Complete Usage Example

Via Cursor/Claude Desktop

Once the MCP server is configured and restarted, you can simply ask:

You: "Download and index this paper about transformers"
Agent: [Automatically uses download_pdf and index_pdf tools]

You: "Search for papers about attention mechanisms"
Agent: [Automatically uses search_research_papers tool]

You: "Show me the Introduction section of the transformer paper"
Agent: [Automatically uses get_document_section tool]

Via OpenAI Agents Framework

from agents import Agent, Runner
from agents.mcp import MCPServerStdio

async with MCPServerStdio(
    name="PDF Indexer",
    params={
        "command": "/path/to/venv/bin/python3",
        "args": ["/path/to/semantic_chunked_pdf_rag.py"],
    },
) as pdf_server:
    agent = Agent(
        name="Research Assistant",
        instructions="""
        You help users manage and search research papers.
        You can download, index, and search papers using the available tools.
        """,
        mcp_servers=[pdf_server],
        model="gpt-4"
    )
    
    # Agent autonomously uses tools
    result = await Runner.run(
        agent,
        "Download this paper, index it, make it searchable, and then search for related work on attention"
    )
    print(result.final_output)

Package Structure

pdf_indexer_mcp/
├── semantic_chunked_pdf_rag.py  # Main MCP server (exposes tools)
├── utils/                       # Logging utilities
├── pdf_processing/             # PDF text extraction
├── chunking/                   # Chunking algorithms
├── database/                    # Database models and operations
├── embeddings/                 # MLX embedding generation and FAISS
├── papers/                     # Downloaded PDFs (created automatically)
├── indexes/                    # Database and FAISS indices (created automatically)
├── logs/                       # Log files (created automatically)
├── requirements.txt            # Python dependencies
├── pyproject.toml              # Package metadata for pip
├── LICENSE                     # GPL-3.0 copyleft license
└── README.md                   # This file

Learning RAG (Retrieval-Augmented Generation)

This MCP server demonstrates a complete RAG (Retrieval-Augmented Generation) pipeline for research papers. Understanding RAG is essential for building effective AI systems that can access and use external knowledge.

What is RAG?

RAG combines information retrieval with language generation, allowing LLMs to:

1. Retrieve relevant information from external sources (here: research papers)

2. Augment the LLM's context with retrieved information

3. Generate responses grounded in retrieved content

Instead of relying solely on pre-trained knowledge, RAG enables systems to answer questions using up-to-date, domain-specific information.

How This Server Implements RAG

This MCP server provides a complete RAG implementation:

1. Document Ingestion (Retrieval Setup)

• download_pdf(): Fetch papers from URLs

• index_pdf(): Extract and chunk text, store in database

• Creates a searchable knowledge base

2. Semantic Indexing (Vector Search)

• generate_embeddings(): Convert text chunks into semantic vectors

• Uses MLX-optimized embeddings (Qwen3-Embedding-0.6B, 1024 dimensions)

• Stores vectors in FAISS for fast similarity search

3. Retrieval (Finding Relevant Content)

• search_research_papers(): Semantic search across all papers

• Finds relevant chunks based on meaning, not just keywords

• Returns ranked results with context

4. Augmentation (Context Enhancement)

• get_document_section(): Retrieve full context from specific sections

• Includes surrounding chunks for better understanding

• Provides metadata (section, page, headers)

5. Generation (LLM Response)

• Agent receives retrieved chunks

• Uses them as context to generate grounded responses

• Responses are based on actual paper content, not just training data

RAG Pipeline Flow

User Query
    ↓
Semantic Search (search_research_papers)
    ↓
Find Relevant Chunks (FAISS vector search)
    ↓
Retrieve Context (get_document_section if needed)
    ↓
Augment LLM Context (pass chunks to LLM)
    ↓
Generate Response (grounded in retrieved content)

Key RAG Concepts Demonstrated

1. Chunking Strategy: Two approaches shown:

   • Header-based: Preserves structure, ideal for academic papers

   • S2 chunking: Spatial-semantic hybrid for unstructured documents

2. Semantic Search: Uses embeddings to find meaning, not just keywords

   • "attention mechanisms" finds related concepts even without exact words

   • Better than traditional keyword search

3. Vector Database: FAISS for fast similarity search

   • Scales to thousands of chunks

   • Sub-millisecond search times

4. Incremental Indexing: Add papers without rebuilding entire index

   • Each paper can be indexed independently

   • Embeddings added incrementally

5. Context Windows: Retrieve surrounding chunks for better context

   • Helps maintain narrative flow

   • Provides background for understanding

Why RAG Matters

Without RAG: LLMs can only use pre-trained knowledge, which may be:

• Outdated (training data cutoff)

• Generic (not domain-specific)

• Limited (no access to private/publications)

With RAG: LLMs can:

• Access current information (newly published papers)

• Use domain-specific knowledge (research papers)

• Ground responses in verifiable sources

• Answer questions about documents not in training data

RAG Best Practices (This Implementation)

1. Effective Chunking: Balance chunk size - too small loses context, too large dilutes relevance

2. Semantic Embeddings: Use models optimized for your domain (here: research papers)

3. Vector Search: Fast retrieval is essential (FAISS provides sub-millisecond search)

4. Metadata Preservation: Keep headers, pages, sections for navigation

5. Context Retrieval: Include surrounding chunks for better understanding

Further Learning

To understand RAG better:

• Experiment with different chunking methods (header vs S2)

• Try different embedding models

• Adjust context_window in search_research_papers()

• Explore the database structure to see how chunks are stored

• Check logs to see performance metrics

This implementation provides a production-ready RAG system you can study and extend.

Requirements

• Python: 3.9+ (required by dependencies like numpy 2.2.6)

• Platform: macOS (for MLX optimization), Linux/Windows (with CPU fallback)

• RAM: ~500MB for embeddings

• Disk: ~1GB for model downloads (first run)

Technical Details

Embedding Model

• Model: mlx-community/Qwen3-Embedding-0.6B

• Dimensions: 1024

• Framework: MLX (optimized for Apple Silicon)

• Speed: ~35 embeddings/second on Apple Silicon

Chunking Methods

Header-based (method="header"):

• Best for academic papers with clear structure

• Preserves document hierarchy

• Groups content under headers

S2 Chunking (method="s2"):

• Hybrid spatial-semantic approach

• Combines layout analysis with semantic similarity

• Optimal for unstructured documents

Storage

• Database: SQLite (indexes/research_papers.db)

• Vector Index: FAISS (indexes/research_papers.faiss)

• Mapping: NumPy array (indexes/research_papers_mapping.npy)

Troubleshooting

MCP Server Not Starting

1. Verify virtual environment:

   which python3  # Should show path in venv/bin/python3

2. Check dependencies:

   pip list | grep fastmcp

3. Test server manually:

   python semantic_chunked_pdf_rag.py

   If it starts without errors, press Ctrl+C to stop.

4. Check logs:

   tail -f logs/pdf_indexer_*.log

Tools Not Appearing in Agent

1. Restart completely (not just reload)

2. Check configuration path is absolute (not relative)

3. Verify Python path points to virtual environment

4. Check MCP logs for connection errors

Embedding Generation Fails

1. Verify MLX installed:

   python -c "import mlx.core as mx; print('OK')"

2. Check available RAM (needs ~500MB)

3. First run downloads model automatically (may take time)

Search Returns No Results

1. Verify papers indexed:

   # Agents should discover list_indexed_papers() tool

2. Generate embeddings:

   # Agents should discover generate_embeddings() tool

3. Check FAISS index exists in indexes/ directory

Contributing

Contributions welcome! This project uses GPL-3.0 copyleft licensing.

License

GNU General Public License v3.0 (GPL-3.0) - Copyleft License

Copyright (C) 2025 Liz Howard (@lizTheDeveloper)

See LICENSE file for full license text.

Links

• Repository: https://github.com/lizTheDeveloper/pdf-indexer-mcp

• Issues: https://github.com/lizTheDeveloper/pdf-indexer-mcp/issues

• Author: Liz Howard (@lizTheDeveloper)