Pdf2mcp

██████╗ ██████╗ ███████╗██████╗ ███╗   ███╗ ██████╗██████╗
██╔══██╗██╔══██╗██╔════╝╚════██╗████╗ ████║██╔════╝██╔══██╗
██████╔╝██║  ██║█████╗   █████╔╝██╔████╔██║██║     ██████╔╝
██╔═══╝ ██║  ██║██╔══╝  ██╔═══╝ ██║╚██╔╝██║██║     ██╔═══╝
██║     ██████╔╝██║     ███████╗██║ ╚═╝ ██║╚██████╗██║
╚═╝     ╚═════╝ ╚═╝     ╚══════╝╚═╝     ╚═╝ ╚═════╝╚═╝

PyPI License: MIT Python 3.10+

Turn any PDF folder into a searchable MCP server with semantic, hybrid, or keyword search.

Installation

From PyPI (recommended)

pip install pdf2mcp

Or with uv:

uv tool install pdf2mcp

From source

git clone https://github.com/iSamBa/pdf2mcp.git
uv tool install ./pdf2mcp

To update after pulling new changes:

uv tool install --force ./pdf2mcp

Optional: Tesseract OCR

Tesseract is only needed if you want to extract text from scanned or image-only PDFs. Without it, pdf2mcp works fine for text-based PDFs — image-only pages are simply skipped with a warning.

macOS:

brew install tesseract

Ubuntu / Debian:

sudo apt-get install tesseract-ocr

Windows:

Download the installer from UB-Mannheim/tesseract.

Additional languages: install language packs for non-English PDFs:

# Example: French and German
sudo apt-get install tesseract-ocr-fra tesseract-ocr-deu
# or on macOS
brew install tesseract-lang

Then set PDF2MCP_OCR_LANGUAGE to the appropriate language code (e.g., fra, deu).

Verify

pdf2mcp --version

Quick Start

Interactive Setup (recommended)

pdf2mcp init -i ./my-project

The interactive wizard walks you through all configuration in 6 steps:

1. Project directory — confirm or change the target path
2. OpenAI API key — securely enter your key (masked input) and optional base URL
3. Documents directory — where your PDFs live (default: docs)
4. Embedding settings — choose model, chunk size, and overlap
5. Server settings — name, transport, host, and port
6. OCR settings — enable/disable OCR for scanned PDFs

After setup, the wizard optionally offers to ingest any PDFs found in your docs directory and generate ready-to-paste MCP client config snippets.

Manual Setup

# 1. Scaffold a project (creates docs/ and .env template)
pdf2mcp init ./my-project
cd my-project

# 2. Add your PDFs to docs/ and set OPENAI_API_KEY in .env

# 3. Ingest
pdf2mcp ingest

# 4. Start the server
pdf2mcp serve

# 5. Get config snippets for your MCP client
pdf2mcp config

Architecture

pdf2mcp separates server and client concerns:

• Server (pdf2mcp serve) — runs independently, handles PDF ingestion, embedding, and search. Configured via PDF2MCP_* environment variables.
• Client (Claude Code, Cursor, VS Code, etc.) — connects to a running server over HTTP. Only needs the server URL.

The default transport is streamable-http. The server listens on http://127.0.0.1:8000/mcp and shuts down gracefully on SIGINT/SIGTERM.

OCR / Scanned PDF Support

pdf2mcp automatically detects image-only pages in PDFs and falls back to Tesseract OCR when available:

• Per-page strategy: text pages are extracted via pymupdf4llm; image-only pages are OCR'd via Tesseract.
• Automatic detection: each page is checked for extractable text (via _page_has_text) and image dominance (via _is_image_dominant). Pages without sufficient text are classified as image-only.
• Graceful degradation: if Tesseract is not installed or OCR is disabled, image-only pages are skipped with a warning — text-based pages are still extracted normally.
• Configuration: use PDF2MCP_OCR_ENABLED, PDF2MCP_OCR_LANGUAGE, and PDF2MCP_OCR_DPI environment variables (see Environment Variables).

Commands

Command	Description
pdf2mcp init [dir]	Scaffold a working directory with docs/ and .env
pdf2mcp init -i [dir]	Launch the interactive setup wizard
pdf2mcp ingest	Parse PDFs, chunk, embed, and store in vector DB
pdf2mcp serve	Start the MCP server (HTTP by default)
pdf2mcp config	Print ready-to-paste config for MCP clients
pdf2mcp stats	Display index statistics (doc count, chunks, DB size)
pdf2mcp search <query>	Search the index from the command line
pdf2mcp delete <filename>	Delete a document from the index

Common Flags

# Override docs directory
pdf2mcp ingest --docs-dir ./my-pdfs
pdf2mcp serve --docs-dir ./my-pdfs

# Force re-ingestion (clears DB and re-ingests all documents)
pdf2mcp ingest --force

# Enable debug logging
pdf2mcp ingest -v
pdf2mcp serve --verbose

# Use stdio transport (for clients that spawn the server)
pdf2mcp serve --transport stdio

# Custom host/port
pdf2mcp serve --host 0.0.0.0 --port 9000

# Custom server name
pdf2mcp serve --name my-docs

# Config for a specific client
pdf2mcp config --client cursor
pdf2mcp config --client claude-desktop --transport stdio

# Interactive setup wizard
pdf2mcp init -i ./my-project
pdf2mcp init --interactive

# View index statistics
pdf2mcp stats

# Search the index from CLI
pdf2mcp search "safety requirements"
pdf2mcp search "torque settings" --filename manual.pdf
pdf2mcp search "installation" -n 10

# Delete a document from the index
pdf2mcp delete old-manual.pdf
pdf2mcp delete old-manual.pdf -y   # skip confirmation

Client Configuration

pdf2mcp config generates ready-to-paste JSON for all supported clients. The default is HTTP — clients just need the server URL:

{
  "mcpServers": {
    "pdf-docs": {
      "type": "http",
      "url": "http://127.0.0.1:8000/mcp"
    }
  }
}

Client	Config File	Top-level Key	HTTP Support
Claude Code	.mcp.json	mcpServers	Yes
Claude Desktop	claude_desktop_config.json	mcpServers	No (stdio only)
Cursor	.cursor/mcp.json	mcpServers	Yes
VS Code / Copilot	.vscode/mcp.json	servers	Yes

Use --transport stdio for clients that need to spawn the server process (e.g., Claude Desktop):

{
  "mcpServers": {
    "pdf-docs": {
      "command": "uv",
      "args": ["run", "pdf2mcp", "serve"]
    }
  }
}

Environment Variables

Server settings (PDF2MCP_*)

These configure the server process. MCP clients never need these.

Variable	Default	Description
OPENAI_API_KEY	(required)	OpenAI API key for embeddings
PDF2MCP_OPENAI_BASE_URL	https://api.openai.com/v1	OpenAI API base URL (for Azure, local proxies, or compatible providers)
PDF2MCP_DOCS_DIR	docs	Directory containing PDF files
PDF2MCP_DATA_DIR	data	Directory for vector database
PDF2MCP_EMBEDDING_MODEL	text-embedding-3-small	OpenAI embedding model
PDF2MCP_CHUNK_SIZE	500	Target chunk size in tokens
PDF2MCP_CHUNK_OVERLAP	50	Overlap between chunks in tokens
PDF2MCP_DEFAULT_NUM_RESULTS	5	Default search results count
PDF2MCP_SERVER_NAME	pdf-docs	MCP server name
PDF2MCP_SERVER_TRANSPORT	streamable-http	Transport protocol
PDF2MCP_SERVER_HOST	127.0.0.1	Host to bind to
PDF2MCP_SERVER_PORT	8000	Port to bind to
PDF2MCP_SEARCH_MODE	semantic	Search mode: semantic, hybrid, or keyword
PDF2MCP_OCR_ENABLED	true	Enable OCR for scanned/image-only pages
PDF2MCP_OCR_LANGUAGE	eng	Tesseract language code
PDF2MCP_OCR_DPI	300	DPI for OCR rendering

Search Modes

pdf2mcp supports three search modes, controlled by the PDF2MCP_SEARCH_MODE environment variable:

Mode	Description	When to use
semantic (default)	Pure vector similarity search	General natural-language queries
keyword	Full-text search (no embeddings needed)	Exact terms, acronyms, error codes
hybrid	Combines vector + full-text search	Best of both worlds

To switch modes, set PDF2MCP_SEARCH_MODE in your .env and re-ingest:

# In .env
PDF2MCP_SEARCH_MODE=hybrid

# Re-ingest to build the FTS index
pdf2mcp ingest --force

Hybrid and keyword modes automatically create a full-text search index. If you switch modes without re-ingesting, the FTS index is created lazily on the first query.

MCP Tools

The server exposes six tools:

Tool	Description
search_docs(query)	Search across all ingested PDFs
search_in_doc(query, filename)	Search scoped to a single document
list_docs()	List all ingested documents with chunk counts
get_sections(filename)	Get section headings for a specific document
read_page(filename, page)	Read the full content of a specific page
read_section(filename, section_title)	Read the full content of a named section

Typical workflow

1. list_docs — discover available documents
2. get_sections — browse a document's structure
3. read_section or read_page — read specific content
4. search_docs or search_in_doc — find information by query

MCP Prompts

The server provides five prompts that guide LLMs through multi-tool workflows:

Prompt	Args	Description
summarize_document	filename	Read all sections and synthesize a summary
compare_documents	filename1, filename2	Side-by-side comparison of two documents
extract_key_findings	filename	Extract conclusions, recommendations, and key findings
deep_dive	filename, topic	Exhaustive analysis of a specific topic
document_overview	filename	Structured table of contents with brief descriptions

Prompts return step-by-step instructions that reference the existing tools, enabling LLMs to perform complex multi-step document analysis automatically.

MCP Resources

Resource URI	Description
docs://status	Server status: document count, chunk count, embedding model, and docs directory

Development

git clone https://github.com/iSamBa/pdf2mcp.git
cd pdf2mcp
uv sync --all-extras
uv run pytest
uv run ruff check src/
uv run mypy src/

License

MIT