mcp-glm-ocr

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Free OCR & vision for any AI coding client, powered by GLM-4.6V-Flash.

mcp-glm-ocr is an MCP server that gives OCR and image-understanding capabilities to AI clients that cannot process images themselves (Claude Code, Claude Desktop, Cursor, Cline, OpenCode, and any other MCP-compatible client).

Under the hood it sends the image to GLM-4.6V-Flash, z.ai's vision-language model, which is completely free (input and output). The model reads the image and returns text, so your text-only client gets full vision capabilities at zero cost.

Why this exists: z.ai's official vision MCP server is exclusive to paid GLM Coding Plan subscribers. This one works with a plain free z.ai account.

Tools

All tools accept:

• a local file path (absolute, or relative to the client's working directory), e.g. demo.png
• an http(s) URL, e.g. https://example.com/photo.jpg
• a file:// URL or a data: URL

Requirements

• Python 3.10+ (or just uv)
• A free z.ai API key

Get a free API key

1. Go to https://z.ai/model-api and create an account (or log in).
2. Create an API key at https://z.ai/manage-apikey/apikey-list.
3. GLM-4.6V-Flash costs nothing to use.

Installation & configuration

1. Claude Code

claude mcp add glm-ocr --env ZAI_API_KEY=your_key -- uvx mcp-glm-ocr

Then in any conversation:

> what does demo.png say?
> read the text from receipt.jpg
> describe this image: ~/Pictures/screenshot.png
> analyze this chart: chart.png — what is the trend?

2. Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "glm-ocr": {
      "command": "uvx",
      "args": ["mcp-glm-ocr"],
      "env": { "ZAI_API_KEY": "your_key" }
    }
  }
}

3. Cursor

In .cursor/mcp.json at the root of your project:

{
  "mcpServers": {
    "glm-ocr": {
      "command": "uvx",
      "args": ["mcp-glm-ocr"],
      "env": { "ZAI_API_KEY": "your_key" }
    }
  }
}

4. Cline (VS Code)

In Cline settings → MCP servers:

{
  "mcpServers": {
    "glm-ocr": {
      "command": "uvx",
      "args": ["mcp-glm-ocr"],
      "env": { "ZAI_API_KEY": "your_key" }
    }
  }
}

5. OpenCode

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "glm-ocr": {
      "type": "local",
      "command": ["uvx", "mcp-glm-ocr"],
      "environment": { "ZAI_API_KEY": "your_key" }
    }
  }
}

Without uv (pip / Docker)

pip install mcp-glm-ocr
mcp-glm-ocr          # requires ZAI_API_KEY env var

docker run --rm -i -e ZAI_API_KEY=your_key mcp-glm-ocr

Environment variables

Development

uv sync --all-groups        # install deps + dev deps
uv run pytest -q            # unit tests (no network, no API key)
uv run python scripts/smoke_stdio.py   # boots the real stdio MCP server, lists tools
uv run mcp-glm-ocr          # run the server locally (needs ZAI_API_KEY)
uv run mcp-glm-ocr --version
uv run python scripts/banner.py   # print the banner in color (terminal)

# real end-to-end call against the z.ai API (needs a real key).
# Omitting the image uses the bundled test fixture (tests/fixtures/test_image.png):
ZAI_API_KEY=your_key uv run python scripts/live_test.py
ZAI_API_KEY=your_key uv run python scripts/live_test.py path/to/your/image.png

How it works

1. The client model calls ocr_image / describe_image / analyze_image.
2. The server resolves the image (local file → base64 data: URL, or passthrough for URLs).
3. It calls POST /chat/completions on the z.ai OpenAI-compatible API with model glm-4.6v-flash.
4. The text answer is returned to the client model.

Limitations

• Images only (no video). Video is out of scope for this free model.
• Free tier has rate limits set by z.ai; check the rate limits docs.
• Very large images are rejected by default (ZAI_MAX_IMAGE_MB=15).

Releasing (maintainer)

Option A: tag a version and let GitHub Actions build and publish automatically:

git tag v0.1.0 && git push --tags

Prerequisite: create a PyPI API token at https://pypi.org/manage/account/token/ and store it as the PYPI secret in the GitHub repo settings.

Option B: publish manually:

uv build                          # build sdist + wheel
uv publish                        # push to PyPI (requires a PyPI token)

After publishing, users install with uvx mcp-glm-ocr or pip install mcp-glm-ocr. Remember to bump version in pyproject.toml and src/mcp_glm_ocr/__init__.py for each release.

License

MIT