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Image Parse MCP

Image Parse MCP

by copylee711
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Image Parse MCP

中文文档

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.)

Related MCP server: Vision MCP Server

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

Available Tools

1 tool
analyze_imageA
Read-onlyIdempotent

Analyze an image with a multimodal LLM (GPT-4o, Claude, Gemini, etc.).

Provide an image (URL, local path, or base64) and a description of what you want to know. The tool calls an OpenAI-compatible vision API and returns the model's text response.

Use this tool whenever you have an image and need to:

  • Describe its contents

  • Extract text / OCR

  • Understand a chart, diagram, or data visualization

  • Analyse a UI screenshot (layout, elements, issues)

  • Identify objects, colours, people, or scenes in a photo

  • Compare or summarise visual information

Args: params (AnalyzeImageInput): - image_source (str): URL, local file path, or base64 image data. - prompt (str): What to analyze or extract from the image. - mime_type (Optional[str]): Override auto-detected MIME type.

Returns: str: The multimodal model's analysis as plain text / Markdown.

Inputs: params (required). Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4.4/5.0

    Scored across 1 tool

    Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap. Agents can unambiguously select the correct tool for any image analysis task.

    Naming Consistency5/5

    The single tool uses a clear verb_noun pattern ('analyze_image'), which is consistent with best practices. No other tools exist to introduce inconsistency.

    Tool Count4/5

    One tool is on the low end for a general utility, but it fits the narrow scope of image parsing via LLM. A single unified tool can be sufficient if it covers the intended use cases without needing multiple specialized tools.

    Completeness4/5

    The tool covers core image analysis needs (description, OCR, chart understanding) through a multimodal LLM. Minor gaps like batch processing or model selection are absent, but the core functionality is well-covered for the stated purpose.

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