Image Parse MCP

A multimodal image analysis MCP server that connects to any OpenAI-compatible vision API. Provide an image (URL, local path, or base64) and a prompt — get back a detailed analysis from the multimodal LLM of your choice.

Supported Providers

Any provider with an OpenAI-compatible chat completions endpoint:

• OpenAI — GPT-4o, GPT-4-vision, GPT-4.1-mini
• Anthropic (via compatible proxy / gateway)
• Google Gemini (via OpenAI-compatible endpoint)
• Azure OpenAI
• Local models (Ollama, vLLM, LM Studio with OpenAI-compatible servers)
• Alibaba Bailian — Qwen-VL (via OpenAI-compatible endpoint)
• Third-party (DeepSeek, Groq, Together.ai, OpenRouter, etc.)

Configuration

Set these environment variables before launching the server:

Variable	Required	Default	Description
IMAGE_PARSE_API_KEY	Yes	—	API key for your provider
IMAGE_PARSE_BASE_URL	No	https://api.openai.com/v1	API base URL
IMAGE_PARSE_MODEL	No	gpt-4o	Multimodal model name

Example: OpenAI

export IMAGE_PARSE_API_KEY=sk-...
export IMAGE_PARSE_BASE_URL=https://api.openai.com/v1
export IMAGE_PARSE_MODEL=gpt-4o

Example: Google Gemini (via AI Studio)

export IMAGE_PARSE_API_KEY=your-gemini-api-key
export IMAGE_PARSE_BASE_URL=https://generativelanguage.googleapis.com/v1beta/openai
export IMAGE_PARSE_MODEL=gemini-2.5-flash

Example: Ollama (local)

export IMAGE_PARSE_API_KEY=ollama
export IMAGE_PARSE_BASE_URL=http://localhost:11434/v1
export IMAGE_PARSE_MODEL=llava

Example: Azure OpenAI

export IMAGE_PARSE_API_KEY=your-azure-api-key
export IMAGE_PARSE_BASE_URL=https://your-resource.openai.azure.com/openai/deployments/your-deployment
export IMAGE_PARSE_MODEL=gpt-4o

Example: Alibaba Bailian (Qwen-VL)

export IMAGE_PARSE_API_KEY=your-dashscope-api-key
export IMAGE_PARSE_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
export IMAGE_PARSE_MODEL=qwen-vl-max

Example: DeepSeek

export IMAGE_PARSE_API_KEY=your-deepseek-api-key
export IMAGE_PARSE_BASE_URL=https://api.deepseek.com/v1
export IMAGE_PARSE_MODEL=deepseek-chat

Install & Run

# Clone or enter the project directory
cd image-parse

# Run directly (uv handles venv + deps automatically)
uv run image-parse-mcp

Claude Code Configuration

Add to your Claude Code MCP config (~/.claude/claude.json or project .mcp.json):

{
  "mcpServers": {
    "image-parse": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "--directory", "path/to/image-parse", "image-parse-mcp"],
      "env": {
        "IMAGE_PARSE_API_KEY": "sk-...",
        "IMAGE_PARSE_BASE_URL": "https://api.openai.com/v1",
        "IMAGE_PARSE_MODEL": "gpt-4o"
      }
    }
  }
}

Tool: analyze_image

Parameter	Required	Description
image_source	Yes	URL, local file path, base64 string, or data URI
prompt	Yes	What to analyze / extract from the image
mime_type	No	Override auto-detected MIME type (e.g. image/webp)

What agents use it for

• Describe the contents of an image
• Extract text from a screenshot (OCR)
• Read and interpret charts, graphs, data visualizations
• Analyze UI screenshots (layout, elements, issues)
• Identify objects, colours, people, or scenes in photos
• Compare visual information across multiple images
• Diagnose errors from error screenshots

Input forms for image_source

# URL
https://example.com/screenshot.png

# Local file path (on the host machine)
/Users/me/Downloads/chart.png

# Base64 data URI
data:image/png;base64,iVBORw0KGgo...

# Raw base64
iVBORw0KGgo...

Development

# Create venv and install deps
uv venv
uv pip install -e .

# Run tests
uv run python -m pytest