Which integrations are available for this server?

Enables text extraction from JPEG images, allowing users to process visual information from local files or web URLs through the OCR tool. Converts extracted text from images and PDF documents into Markdown format by default for easy use in documentation and workflows.

How do I use GLM OCR MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@GLM OCR MCP Server extract the text from document.pdf" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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