PDF2ZH Server

A slim MCP (Model Context Protocol) tool server that translates scientific PDF documents while preserving formulas, charts, table of contents, and layout. Designed to be launched and managed by FeatherFlow.

Based on PDFMathTranslate, stripped down to a single OpenAI-compatible translation backend — reusing the same LLM that FeatherFlow is already connected to.

Features

• MCP stdio transport — plug-and-play with FeatherFlow (or any MCP-compatible host)
• Preserves formulas & layout — powered by ONNX-based document layout analysis + pdfminer/pymupdf
• Dual output — generates both mono (translated-only) and dual (bilingual side-by-side) PDFs
• Shares FeatherFlow's LLM — OpenAI-compatible endpoint via environment variables, no extra API key needed
• Cross-platform — works on Linux and Windows; all paths use pathlib for portability

MCP Tools

Tool	Description
translate_pdf	Translate a PDF file. Accepts file, lang_in, lang_out, optional output_dir. Returns absolute paths to mono & dual PDFs.
list_supported_languages	List all supported language codes (en, zh, ja, ko, fr, de, etc.)

File Path Resolution

The file parameter of translate_pdf supports both absolute and relative paths:

• Absolute path — used as-is (e.g. /home/user/.featherflow/workspace/paper.pdf)
• Relative path — resolved against the workspace directory (defaults to ~/.featherflow/workspace, overridable via the WORKSPACE_DIR environment variable)

Output PDFs are written to the workspace directory by default. The returned paths are always absolute, making them directly usable by other MCP tools (e.g. feishu-mcp upload_file / upload_file_and_share).

Requirements

⚠️ Python Environment Isolation — Important

This project depends on babeldoc / onnxruntime, which require Python ≥3.10, <3.13. FeatherFlow itself may run on a different Python version (e.g. 3.13+). You must create a separate Python environment for this project and point FeatherFlow's MCP config to this project's Python executable — not FeatherFlow's own Python.

• Python 3.10 – 3.12 (recommended: 3.12)
• uv (recommended), Conda, or virtualenv for environment isolation

Installation

0. Install uv (one-time setup, recommended)

uv can automatically download and manage any Python version — no need to install Python 3.12 manually.

# Linux / macOS
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows PowerShell
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Restart your terminal after installation, then verify:

uv --version

1. Create a dedicated Python 3.12 environment

Using uv (recommended — auto-downloads Python 3.12 even if you only have 3.13+):

cd /path/to/pdftranslate-mcp
uv venv .venv --python 3.12

Activate the environment:

# Linux / macOS
source .venv/bin/activate

# Windows PowerShell
.venv\Scripts\Activate.ps1

# Windows CMD
.venv\Scripts\activate.bat

Alternative: Conda

conda create -p /path/to/pdftranslate-mcp/.venv python=3.12 -y
conda activate /path/to/pdftranslate-mcp/.venv

Alternative: venv (only if system Python is already 3.10–3.12)

python3.12 -m venv .venv
source .venv/bin/activate        # Linux/macOS
# .venv\Scripts\Activate.ps1     # Windows PowerShell

2. Install the package

pip install -e .

Or with uv (10-100x faster):

uv pip install -e .

This installs all dependencies: pymupdf, pdfminer-six, babeldoc, onnxruntime, openai, mcp, etc.

3. Verify

python -m pdf2zh.mcp_server --help

FeatherFlow Configuration

Edit ~/.featherflow/config.json (or the config file for your setup). Add pdf2zh under tools.mcpServers.

Key point: The command must point to this project's own Python executable, not FeatherFlow's Python. This project requires Python <3.13, while FeatherFlow may run on a newer version.

Example (Linux — production server)

{
  "tools": {
    "mcpServers": {
      "pdf2zh": {
        "command": "/opt/PDFMathTranslate/.venv/bin/python",
        "args": ["-m", "pdf2zh.mcp_server"],
        "toolTimeout": 600,
        "env": {
          "OPENAI_BASE_URL": "https://openrouter.ai/api/v1",
          "OPENAI_API_KEY": "sk-or-v1-xxxxxxxx",
          "OPENAI_MODEL": "anthropic/claude-opus-4-5"
        }
      }
    }
  }
}

Example (Windows — development)

{
  "tools": {
    "mcpServers": {
      "pdf2zh": {
        "command": "C:/Users/<you>/code/PDFMathTranslate/.venv/python.exe",
        "args": ["-m", "pdf2zh.mcp_server"],
        "toolTimeout": 600,
        "env": {
          "OPENAI_BASE_URL": "https://openrouter.ai/api/v1",
          "OPENAI_API_KEY": "sk-or-v1-xxxxxxxx",
          "OPENAI_MODEL": "anthropic/claude-opus-4-5"
        }
      }
    }
  }
}

Tip: On Windows, use forward slashes / in JSON paths — they work fine with Python's pathlib.

Tool Timeout — Critical for PDF Translation

⚠️ You MUST set toolTimeout for the pdf2zh MCP server.

FeatherFlow's default MCP tool timeout is 30 seconds. PDF translation is a heavy operation — a 6-page paper typically takes 1–5 minutes depending on the LLM speed. Without increasing toolTimeout, the tool call will be cancelled mid-translation, and the agent will report a failure.

Recommended: "toolTimeout": 600 (10 minutes). For very long documents (50+ pages), consider 1200 (20 minutes).

"pdf2zh": {
  "command": "...",
  "args": ["-m", "pdf2zh.mcp_server"],
  "toolTimeout": 600,
  ...
}

Environment Variables

Variable	Required	Description
OPENAI_BASE_URL	Yes	API base URL (e.g. https://openrouter.ai/api/v1, https://api.openai.com/v1)
OPENAI_API_KEY	Yes	API key for the provider
OPENAI_MODEL	Yes	Model identifier (e.g. anthropic/claude-opus-4-5, gpt-4o)
OPENAI_TEMPERATURE	No	Override LLM temperature. Omit for reasoning models (e.g. kimi-k2.5) which enforce their own temperature. Set to 0 for deterministic output with standard models.
WORKSPACE_DIR	No	Shared workspace directory. Defaults to ~/.featherflow/workspace (same as FeatherFlow's built-in file tools and paper_download). Override this if your workspace is at a non-standard location.

The OPENAI_* variables are the same credentials FeatherFlow uses — just pass them through via env. WORKSPACE_DIR usually does not need to be set; it automatically uses FeatherFlow's default workspace.

Standalone Usage (without FeatherFlow)

stdio mode (default)

python -m pdf2zh.mcp_server

SSE mode (for web-based MCP clients)

python -m pdf2zh.mcp_server --sse --host 0.0.0.0 --port 3001

Cross-MCP Workflow: pdf2zh + feishu-mcp

This project is designed to work alongside feishu-mcp. A typical end-to-end flow:

User: "Translate this paper and share it in the Feishu group"
  ↓
FeatherFlow (LLM orchestration):
  1. paper_download → ~/.featherflow/workspace/paper.pdf
  2. pdf2zh.translate_pdf(file="paper.pdf", lang_in="en", lang_out="zh")
     → ~/.featherflow/workspace/paper-mono.pdf
     → ~/.featherflow/workspace/paper-dual.pdf
  3. feishu-mcp.upload_file_and_share(file_path="/home/user/.featherflow/workspace/paper-dual.pdf")
     → share_url
  4. feishu-mcp.send_message(chat_id, share_url)

Why this works seamlessly:

• pdf2zh writes output to ~/.featherflow/workspace by default
• feishu-mcp upload_file / upload_file_and_share accepts absolute file paths
• pdf2zh returns absolute paths in its result — the LLM can extract and pass them directly to feishu-mcp
• Both MCP servers run as local processes on the same machine, sharing the same filesystem

Project Structure

pdf2zh/
  __init__.py        # Package entry, exports translate_stream
  mcp_server.py      # MCP server (entry point, tools definition)
  translator.py      # BaseTranslator + OpenAITranslator
  converter.py       # PDF content conversion & layout processing
  high_level.py      # Core translation pipeline (translate_stream)
  config.py          # Configuration & constants
  cache.py           # Translation cache (SQLite via peewee)
  doclayout.py       # ONNX document layout model loading
  pdfinterp.py       # Extended PDF interpreter
pyproject.toml       # Dependencies & build config

License

AGPL-3.0

Credits

• Core PDF translation engine from PDFMathTranslate by Byaidu
• MCP host integration for FeatherFlow