Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
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.
MCP server for extracting text from images and PDFs using ZhipuAI GLM-OCR.
{
"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"
}
}
}
}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-mcpAdd 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-mcpThe 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.
# 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)Related MCP server: OCR MCP Service
# 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.serverWindows PowerShell activation:
.venv\Scripts\Activate.ps1glm-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 serverExtract text from local files or URLs. Supported formats: PNG, JPG/JPEG, PDF.
Inputs: file_path, base64_data, start_page_id, end_page_id, +1 more.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 1 tool
Only one tool exists, so there is no possibility of confusing it with another. Its purpose is clearly described as extracting text from files or URLs.
The single tool name 'extract_text' follows a clear verb_noun pattern. With only one tool, there are no inconsistencies to assess.
A single tool serves a narrow OCR purpose, but by the rubric 1-2 tools feels thin. It is functional but borderline for a full MCP server.
The tool covers the core OCR workflow for common formats (PNG, JPG, PDF) from both local files and URLs. It lacks advanced features like batch processing or layout detection, but these are not essential for basic text extraction.
Document conversion and OCR for AI agents: PDF, Office docs, images to text.
Convert PDF, Word, Excel and scanned documents to Markdown, tables and RAG chunks. OCR.
OCR text from images and scanned PDFs; pay-per-event via your Apify account.
OCR and document understanding: extract text from images, then summarize or translate it.