GLM Vision MCP

<p align="center"> <strong>Give any text-only reasoning model a pair of eyes.</strong> </p>

<p align="center"> An MCP (Model Context Protocol) server that wraps vision-language models — like <strong>GLM-4.6V-Flash</strong> — as callable tools, so text-only LLMs (DeepSeek-R1, Qwen3-Thinking, etc.) can <em>see</em> images inside Cursor, Trae CN, Cline, Claude Desktop, and any MCP-compatible agent. </p>

---

How It Works

┌─────────────────────────────────────────────────────────┐
│                    Your IDE / Agent                     │
│                                                         │
│  Text-only model (e.g. DeepSeek-R1)                      │
│       │                                                 │
│       │  "What's in this screenshot?"                    │
│       ▼                                                 │
│  MCP Tool: see_image(path, question)                    │
│       │                                                 │
│       ▼  (stdio / MCP protocol)                         │
│  ┌──────────────────────────────────────────────────┐   │
│  │           GLM Vision MCP Server                  │   │
│  │                                                  │   │
│  │  Reads image → base64 → sends to vision model   │   │
│  │  (GLM-4.6V-Flash / GPT-4o / any VLM)            │   │
│  │                                                  │   │
│  │  Returns: "A login error dialog showing..."      │   │
│  └──────────────────────────────────────────────────┘   │
│       │                                                 │
│       ▼                                                 │
│  Text-only model continues reasoning with vision data   │
└─────────────────────────────────────────────────────────┘

You configure 3 things: model provider, model ID, and API key. The server handles everything else — image reading, base64 encoding, API calls, error handling.

---

Quick Start

1. Install

# From PyPI (once published) or from source:
pip install -e .

Or use directly with uv / pipx without installing:

# Using uv (recommended for MCP)
uv run glm-vision-mcp

2. Configure

The server reads configuration from environment variables:

Variable	Required	Default	Description
VISION_API_KEY	✅ Yes	—	Your model provider's API key
VISION_MODEL_ID	✅ Yes	glm-4.6v-flash	The vision model to use
VISION_MODEL_PROVIDER	✅ Yes	zhipu	Provider name (see table below)
VISION_BASE_URL	❌ No	provider default	Custom API base URL
VISION_MAX_TOKENS	❌ No	2048	Max response tokens
VISION_TEMPERATURE	❌ No	0.4	Sampling temperature

3. Add to Your IDE

Cursor

Add to ~/.cursor/mcp.json (or .cursor/mcp.json in your project):

{
  "mcpServers": {
    "glm-vision": {
      "command": "python",
      "args": ["-m", "glm_vision_mcp"],
      "env": {
        "VISION_MODEL_PROVIDER": "zhipu",
        "VISION_MODEL_ID": "glm-4.6v-flash",
        "VISION_API_KEY": "your-zhipu-api-key-here"
      }
    }
  }
}

Trae CN

Add to Trae's MCP settings (设置 → MCP):

{
  "mcpServers": {
    "glm-vision": {
      "command": "python",
      "args": ["-m", "glm_vision_mcp"],
      "env": {
        "VISION_MODEL_PROVIDER": "zhipu",
        "VISION_MODEL_ID": "glm-4.6v-flash",
        "VISION_API_KEY": "your-zhipu-api-key-here"
      }
    }
  }
}

Cline (VS Code)

Add to ~/.cline/mcp_settings.json:

{
  "mcpServers": {
    "glm-vision": {
      "command": "python",
      "args": ["-m", "glm_vision_mcp"],
      "env": {
        "VISION_MODEL_PROVIDER": "zhipu",
        "VISION_MODEL_ID": "glm-4.6v-flash",
        "VISION_API_KEY": "your-zhipu-api-key-here"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "glm-vision": {
      "command": "python",
      "args": ["-m", "glm_vision_mcp"],
      "env": {
        "VISION_MODEL_PROVIDER": "zhipu",
        "VISION_MODEL_ID": "glm-4.6v-flash",
        "VISION_API_KEY": "your-zhipu-api-key-here"
      }
    }
  }
}

Generic MCP Client (any MCP-compatible tool)

{
  "mcpServers": {
    "glm-vision": {
      "command": "python",
      "args": ["-m", "glm_vision_mcp"],
      "env": {
        "VISION_MODEL_PROVIDER": "zhipu",
        "VISION_MODEL_ID": "glm-4.6v-flash",
        "VISION_API_KEY": "your-api-key"
      }
    }
  }
}

Tip: If you installed via uv, use "command": "uv" and "args": ["run", "glm-vision-mcp"] instead.

---

Supported Providers

Provider	VISION_MODEL_PROVIDER	Default Base URL	Example Models
Zhipu (智谱)	zhipu	https://open.bigmodel.cn/api/paas/v4	glm-4.6v-flash, glm-4v-plus
OpenAI	openai	https://api.openai.com/v1	gpt-4o, gpt-4o-mini
DeepSeek	deepseek	https://api.deepseek.com/v1	deepseek-vl
Moonshot (Kimi)	moonshot	https://api.moonshot.cn/v1	moonshot-v1-8k-vision
SiliconFlow	siliconflow	https://api.siliconflow.cn/v1	Qwen/Qwen2-VL-72B
Custom	custom	(you set VISION_BASE_URL)	Any OpenAI-compatible VLM

Using a Custom Endpoint

Set VISION_BASE_URL to point to your own server (vLLM, Ollama, LM Studio, etc.):

{
  "env": {
    "VISION_MODEL_PROVIDER": "custom",
    "VISION_MODEL_ID": "your-model-name",
    "VISION_API_KEY": "any-or-empty",
    "VISION_BASE_URL": "http://localhost:8000/v1"
  }
}

---

Tools

The server exposes 4 tools. Your IDE's agent will automatically call them when it needs vision:

see_image — Core Vision Tool

Ask any question about an image.

see_image(image, question="What is in this image?")

• image: File path, URL, or base64 string
• question: What you want to know (default: "What is in this image?")

describe_image — Image Description

Generate a text description/caption.

describe_image(image, detail_level="detailed")

• detail_level: "brief" | "detailed" | "exhaustive" (default: "detailed")

extract_text — OCR

Extract all visible text from an image.

extract_text(image, language_hint="Chinese")

• language_hint: Optional — e.g. "Chinese", "English", "mixed"

analyze_chart — Chart & Diagram Analysis

Analyze charts, graphs, architecture diagrams, or UI screenshots.

analyze_chart(image, question="")

• question: Optional specific question (default: general analysis)

---

Usage Example

Once configured, just talk to your IDE's agent normally:

You: "Look at the screenshot at /tmp/error.png — what's wrong?"

The agent will:

1. Call see_image("/tmp/error.png", "What error is shown?")
2. The MCP server sends the image to GLM-4.6V-Flash
3. GLM returns: "The dialog shows a 'Connection Refused' error..."
4. Your text-only model uses that answer to help you

---

Getting an API Key

Zhipu (智谱) — Free Tier Available

1. Visit https://open.bigmodel.cn
2. Sign up / log in
3. Go to API Keys → Create new key
4. Copy the key (format: xxxxxxxx.xxxxxxxx)

GLM-4.6V-Flash offers free quota — great for testing!

OpenAI

1. Visit https://platform.openai.com/api-keys
2. Create a new key

---

Development

Project Structure

glm-vision-mcp/
├── src/glm_vision_mcp/
│   ├── __init__.py
│   ├── __main__.py          # python -m glm_vision_mcp
│   ├── server.py            # MCP server + tool definitions
│   ├── config.py            # Env-var config loader
│   ├── utils.py             # Image encoding utilities
│   └── providers/
│       ├── base.py          # VisionProvider (shared HTTP logic)
│       ├── zhipu.py         # Zhipu GLM provider
│       ├── openai_compat.py # Generic OpenAI-compatible provider
│       └── registry.py      # Provider name → class mapping
├── examples/
│   └── quickstart.py
├── pyproject.toml
├── requirements.txt
└── README.md

Running Tests

pip install -e ".[dev]"
pytest

Adding a New Provider

1. Create src/glm_vision_mcp/providers/my_provider.py:

from glm_vision_mcp.providers.base import VisionProvider

class MyProvider(VisionProvider):
    def _build_headers(self):
        # Custom auth if needed
        return {"X-Api-Key": self.config.api_key}

2. Register in providers/registry.py:

_PROVIDERS["my_provider"] = MyProvider

---

FAQ

Q: Can I use this with a non-vision model? No — the configured model must support vision input (images). If you're unsure, GLM-4.6V-Flash is a good free option.

Q: Does it work with local images? Yes. Pass a file path and the server will read and base64-encode it automatically.

Q: How fast is it? Depends on the model provider. GLM-4.6V-Flash is very fast (typically 1-3 seconds per image).

Q: Can multiple images be analyzed at once? Currently each tool call handles one image. For multi-image comparison, call see_image multiple times or extend the tools.

---

License

MIT © 2026 xiayuyang750