pdf2mcp 0.2.2

Turn any PDF folder into a searchable MCP server

pdf2mcp

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Turn any PDF folder into a searchable MCP server.

Installation

Clone the repo, then install globally with uv tool:

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

This makes pdf2mcp available as a command anywhere on your system.

To update after pulling new changes:

uv tool install --force ./pdf2mcp

To run directly from source without installing:

cd ./pdf2mcp
uv run pdf2mcp --help

Verify

pdf2mcp --version

Quick Start

# 1. Scaffold a project (creates docs/ and .env)
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.

Commands

Command	Description
pdf2mcp init [dir]	Scaffold a working directory with docs/ and .env
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

Common Flags

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

# 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

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

Client settings (PDF2MCP_CLIENT_*)

These configure how a client connects to the server. No secrets needed.

Variable	Default	Description
PDF2MCP_CLIENT_SERVER_NAME	pdf-docs	Server name in client config
PDF2MCP_CLIENT_SERVER_URL	http://127.0.0.1:8000/mcp	Server URL
PDF2MCP_CLIENT_TRANSPORT	streamable-http	Transport protocol

MCP Tools

The server exposes six tools:

Tool	Description
search_docs(query)	Semantic search across all ingested PDFs
search_in_doc(query, filename)	Semantic 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

Development

uv sync --all-extras
uv run pytest
uv run ruff check src/
uv run mypy src/