GLM OCR MCP Server

GLM OCR MCP Server

MCP server for extracting text from images and PDFs using ZhipuAI GLM-OCR.

Usage

{
  "mcpServers": {
    "glm-ocr": {
      "command": "uvx",
      "args": ["glm-ocr-mcp"],
      "env": {
        "ZHIPU_API_KEY": "your_api_key_here",
        "ZHIPU_OCR_API_URL": "https://open.bigmodel.cn/api/paas/v4/layout_parsing"
      }
    }
  }
}

Using with Claude Code

claude mcp add --scope user glm-ocr \
  --env ZHIPU_API_KEY=your_api_key_here \
  --env ZHIPU_OCR_API_URL=https://open.bigmodel.cn/api/paas/v4/layout_parsing \
  -- uvx glm-ocr-mcp

Using with Codex

Add MCP server with command:

codex mcp add glm-ocr \
  --env ZHIPU_API_KEY=your_api_key_here \
  --env ZHIPU_OCR_API_URL=https://open.bigmodel.cn/api/paas/v4/layout_parsing \
  -- uvx glm-ocr-mcp

Tools

The server provides one tool:

• extract_text: Extract from local file or URL (png, jpg/jpeg, pdf)
  • default returns Markdown text
  • set return_json=true to return structured JSON without md_results (contains page parsing details like bbox_2d, content, label, etc.)

Parameters:

• file_path: Local file path or URL for png, jpg/jpeg, or pdf
• base64_data: Optional data URL/base64 payload (use when file_path is unavailable)
• start_page_id: Optional PDF start page (1-based, only effective for PDF)
• end_page_id: Optional PDF end page (1-based, only effective for PDF)
• return_json: Optional boolean, default false. true returns JSON; false returns Markdown.

Examples

# Extract text from local image
extract_text(file_path="./screenshot.png")

# Extract text from local PDF
extract_text(file_path="./document.pdf")

# Extract text from URL image
extract_text(file_path="https://example.com/test.jpg")

# Use base64/data URL
extract_text(base64_data="data:image/png;base64,iVBORw0KGgo...")

# Extract structured layout JSON
extract_text(file_path="https://example.com/test.png", return_json=True)

Development

# Create virtual environment
uv venv
source .venv/bin/activate

# Sync dependencies and install current project
uv sync

# Run server for testing
python -m glm_ocr_mcp.server

Windows PowerShell activation:

.venv\Scripts\Activate.ps1

Project Structure

glm-ocr-mcp/
├── pyproject.toml         # Project configuration
├── README.md              # Documentation
├── .env.example           # Environment variable template
├── src/
│   └── glm_ocr_mcp/
│       ├── __init__.py
│       ├── __main__.py    # Entry point
│       ├── ocr.py         # OCR client
│       └── server.py      # MCP server