Image Summarization MCP Server

A Model Context Protocol (MCP) server that accepts image files and sends them to an OpenAI-compatible chat completion endpoint for analysis, description, and comparison tasks.

Use Case

Many LLMs used for agentic coding are text-only and lack support for image inputs. This tool allows you to use a secondary model dedicated to describing and analyzing images, without having to use a multi-modal LLM for your primary model. It supports both cloud and local LLMs via any server that supports the OpenAI chat completion endpoint (including llama.cpp / llama-swap, Ollama, open-webui, OpenRouter, etc).

For local models, gemma3:4b-it-qat works quite well with a relatively small footprint and fast performance (even on CPU-only).

Features

• Accepts images via unified image_path parameter — local paths, URLs, and data URLs

• Supports task parameter to perform specific analysis beyond general description

• Sends images to OpenAI-compatible chat completion endpoints

• Returns detailed image descriptions

• Configurable endpoint URL, API key, and model

• Optional persistent config file at ~/.config/image_mcp/config.json

• Command-line interface for configuration

• Comprehensive error handling

Quick install from NPM

Add this to your global mcp_settings.json or project mcp.json:

{
  "mcpServers": {
    "image_mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@karlcc/image_mcp"
      ],
      "env": {
        "OPENAI_API_KEY": "YOUR_API_KEY",
        "OPENAI_BASE_URL": "https://api.openai.com/v1",
        "OPENAI_MODEL": "gemini-3.1-flash-lite-preview"
      }
    }
  }
}

If you prefer claude mcp add-json, use:

claude mcp add-json image_mcp --scope user '{
  "type": "stdio",
  "command": "npx",
  "args": ["-y", "@karlcc/image_mcp"],
  "env": {
    "OPENAI_API_KEY": "YOUR_API_KEY",
    "OPENAI_BASE_URL": "https://api.openai.com/v1",
    "OPENAI_MODEL": "gemini-3.1-flash-lite-preview"
  }
}'

At a minimum, configure base URL, API key, and model for your chosen backend.

For use with slow local models, you may need to also increase the timeout and max retries settings.

Configuration

The MCP server can be configured using a config file, environment variables, or command-line arguments.

Environment Variables

• OPENAI_API_KEY: Your API key for the OpenAI-compatible service

• OPENAI_BASE_URL: The base URL of the OpenAI-compatible service (default: http://localhost:9292/v1)

• OPENAI_MODEL: The model to use for image analysis

• OPENAI_TIMEOUT: Request timeout in milliseconds (default: 60000). When running local models you may need to increase this.

• OPENAI_MAX_RETRIES: Maximum number of retry attempts (default: 3)

• OPENAI_STREAMING: Enable/disable streaming (true/false)

• MCP_USE_HTTP: Enable HTTP/SSE transport (true/false)

• MCP_PORT: HTTP port for MCP server (default: 8080)

• IMAGE_MCP_CONFIG_PATH: Override config file path (default: ~/.config/image_mcp/config.json)

Command Line Arguments

npx -y @karlcc/image_mcp \
  --api-key your-api-key \
  --base-url https://api.openai.com/v1 \
  --model gpt-4-vision-preview \
  --http \
  --mcp-port 8080 \
  --timeout 60000 \
  --max-retries 5

Configuration Priority

1. Command-line arguments

2. Environment variables

3. Config file (~/.config/image_mcp/config.json)

4. Default values

Persistent Config

Save your resolved configuration once and reuse it across sessions:

node build/index.js \
  --api-key your-api-key \
  --base-url https://api.openai.com/v1 \
  --model gpt-4.1-mini \
  --http \
  --mcp-port 8080 \
  --save-config

This writes ~/.config/image_mcp/config.json (or a custom file via --config /path/to/config.json).

Verifying your model has vision

Before committing to a model, verify it can actually see images:

# Automatic: --save-config verifies vision by default before writing
node build/index.js --model your-model --save-config

# Quick one-shot check:
IMAGE_MCP_SMOKE=1 npm run test:smoke

# Opt-in startup probe (warns in stderr if model can't see):
IMAGE_MCP_PROBE_ON_START=true node build/index.js

If verification fails, the config file is not written and the exit code is non-zero. Use --no-verify to skip the check.

Usage

Host model vs vision backend

When the host LLM (e.g. GLM-5.1, Claude Haiku) is text-only, it cannot inspect pixels. Wire image_mcp to a vision-capable backend and the host will route image tasks there automatically.

Z.AI / GLM example

npx -y @karlcc/image_mcp \
  --base-url https://open.bigmodel.cn/api/paas/v4 \
  --api-key $ZAI_API_KEY \
  --model glm-4.6v-flash

The app stays backend-agnostic — any OpenAI-compatible endpoint works. glm-4.6v-flash is shown because it is a capable, low-latency vision model available on Z.AI.

Client routing snippet

Add to your MCP client config (e.g. Claude Desktop, Cursor, or .claude/settings.json):

{
  "mcpServers": {
    "image_mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@karlcc/image_mcp@latest"
      ],
      "env": {
        "OPENAI_API_KEY": "YOUR_ZAI_KEY",
        "OPENAI_BASE_URL": "https://open.bigmodel.cn/api/paas/v4",
        "OPENAI_MODEL": "glm-4.6v-flash"
      }
    }
  }
}

MCP Tools

read_image_via_vision_backend

Reads and analyzes one image via the vision backend. Accepts local absolute paths, http(s) URLs, and data URLs.

Parameters

• image_path (string): Image to analyze. Supports:

  • Absolute local paths (e.g. /Users/me/screenshot.png)

  • HTTP/HTTPS URLs (e.g. https://example.com/image.jpg)

  • Data URLs with base64 encoded images (e.g. data:image/png;base64,...)

• task (string, optional): What to do with the image (e.g. "Read all text", "Describe the UI layout", "Extract data from chart"). Defaults to a general description.

Example Usage

Using file path:

{
  "name": "read_image_via_vision_backend",
  "arguments": {
    "image_path": "/Users/me/screenshot.png",
    "task": "Read all text in this screenshot"
  }
}

Using HTTP URL:

{
  "name": "read_image_via_vision_backend",
  "arguments": {
    "image_path": "https://example.com/image.jpg"
  }
}

compare_images_via_vision_backend

Compares 2 or more images via the vision backend. Accepts local absolute paths, http(s) URLs, and data URLs.

Parameters

• image_paths (array of strings, min 2): Images to compare. Each entry supports the same formats as image_path above.

• task (string, optional): What to compare (e.g. "Describe UI differences", "Which chart shows higher values?"). Defaults to a general comparison.

Example Usage

{
  "name": "compare_images_via_vision_backend",
  "arguments": {
    "image_paths": [
      "/Users/me/before.png",
      "/Users/me/after.png"
    ],
    "task": "Describe the UI differences between these screenshots"
  }
}

get_config_info

Returns the active server configuration for diagnostics with the API key redacted.

Dev Setup

1. Clone the repository:

git clone https://github.com/karlcc/image_mcp.git
cd image_mcp

2. Install dependencies:

npm install

3. Build the project:

npm run build

4. Starting the Server

node build/index.js

The server will start and listen on stdio for MCP protocol communications.

To run with HTTP/SSE transport:

node build/index.js --http --mcp-port 8080

MCP Tool Installation (local dev build)

Add this to your global mcp_settings.json or project mcp.json:

{
  "mcpServers": {
    "image_mcp": {
      "command": "node",
      "args": [
        "/path/to/image_mcp/build/index.js"
      ],
      "env": {
        "OPENAI_API_KEY": "YOUR_API_KEY",
        "OPENAI_BASE_URL": "http://localhost:9292/v1",
        "OPENAI_MODEL": "gemma3:4b-it-qat"
      }
    }
  }
}

Testing

Running Tests

Run the test suite:

npm test

The test suite includes:

• Unit tests for image processing functionality

• Integration tests that require a mock server

• Tests for both read_image_via_vision_backend and compare_images_via_vision_backend tools

Model Benchmark (Accuracy + Latency)

Run the built-in benchmark to compare candidate models with weighted accuracy and response latency:

npm run benchmark:models

By default this uses:

• Task file: bench/tasks.default.json

• Models: ~/.config/image_mcp/model_candidates.json (candidates array)

• Ranking: weighted accuracy (desc), success rate (desc), median latency (asc)

Useful overrides:

node scripts/benchmark-models.mjs \
  --models gemma-4-31b,kimi-k2.5-fw,qwen3.5-397b-fw \
  --repeats 2 \
  --tasks bench/tasks.default.json

Outputs:

• Raw call-level results at /tmp/image_mcp_accuracy_benchmark_*.jsonl

• Summary at /tmp/image_mcp_accuracy_summary_*.json

• Auto-updates active model in ~/.config/image_mcp/config.json (disable with --no-update-config)

Mock Server Testing

The project includes a mock OpenAI-compatible server for testing purposes.

1. Start the mock server in a separate terminal:

node tests/mock-server.js

The mock server will start on http://localhost:9293 and provides endpoints for:

• GET /v1/models - Lists available models

• POST /v1/chat/completions - Mock chat completions with image support

• POST /v1/test/image-process - Test endpoint for image processing validation

2. Set environment variables for the mock server:

export OPENAI_BASE_URL=http://localhost:9293/v1
export OPENAI_API_KEY=test-key
export OPENAI_MODEL=test-model-vision

3. Run the integration tests:

npm test tests/integration.test.ts

Real OpenAI-Compatible Server Testing

To test with a real OpenAI-compatible endpoint:

1. Set up your environment variables:

export OPENAI_API_KEY=your-actual-api-key
export OPENAI_BASE_URL=https://api.openai.com/v1
export OPENAI_MODEL=gpt-4-vision-preview

Or for other OpenAI-compatible services:

export OPENAI_API_KEY=your-service-api-key
export OPENAI_BASE_URL=https://your-service-endpoint/v1
export OPENAI_MODEL=your-vision-model

2. Start the MCP server:

node build/index.js --http --mcp-port 8080

3. Send test requests using an MCP client or test the tools directly.

Manual Testing

You can manually test the MCP server using tools like curl or MCP clients:

# Test with a local image file
curl -X POST http://localhost:8080/sse \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
      "name": "read_image_via_vision_backend",
      "arguments": {
        "image_path": "/path/to/your/test/image.jpg"
      }
    }
  }'

API Reference

OpenAI-Compatible API Integration

The server sends requests to the OpenAI-compatible chat completion endpoint with the following structure:

{
  "model": "your-model",
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Describe this image in detail, including all text."
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "data:image/png;base64,..."
          }
        }
      ]
    }
  ],
  "stream": false
}

Supported Image Formats

• JPEG (.jpg, .jpeg)

• PNG (.png)

• GIF (.gif)

• WebP (.webp)

• SVG (.svg)

• BMP (.bmp)

• TIFF (.tiff)

Error Handling

The server includes comprehensive error handling for:

• Invalid image files

• Unsupported image formats

• Missing API keys

• Network connectivity issues

• API response errors

Development

Project Structure

src/
├── config.ts          # Configuration management
├── image-processor.ts # Image processing utilities
├── index.ts          # Main MCP server
└── openai-client.ts  # OpenAI-compatible API client

Building

npm run build

Testing

npm test

Vision smoke test (requires API credentials):

IMAGE_MCP_SMOKE=1 npm run test:smoke

Full preflight before release:

npm run preflight

Release: tag and publish to npm

Recommended flow: GitHub Actions trusted publishing (OIDC).

One-time setup (npm package owner):

# Requires npm v11.10+ and package 2FA enabled on npm.
# If local npm is older, run via npx as shown here.
npx -y npm@latest trust github @karlcc/image_mcp \
  --repo karlcc/image_mcp \
  --file publish.yml \
  --yes

Then ship each release with:

# 1) Verify quality gates
npm run build
npm test

# 2) Commit pending changes
git add -A
git commit -m "chore(release): prepare next version"

# 3) Bump version + create git tag (patch/minor/major)
npm version patch

# 4) Push commit + tag (GitHub Actions publishes to npm)
git push origin main --follow-tags

Fallback manual publish (if trusted publishing is not configured):

npm publish --access public --otp <6-digit-otp>

Dev cycle: four layers of vision detection

The repo is designed so a non-vision model can't slip through silently:

License

This project is licensed under the MIT License.

Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests

5. Submit a pull request

Support

For issues and questions, please open an issue on the GitHub repository.

Tips

Tips / donations always appreciated to help fund future development.