image-recognition-mcp
Enables running an OpenAI-compatible vision-language model on local images with natural-language prompts, returning the model's text response.
README
image-recognition-mcp
An MCP (Model Context Protocol) server that exposes a single tool, vlm_recognize, for running an OpenAI-compatible vision-language model on a local image with a natural-language prompt.
Tool
vlm_recognize
| Parameter | Type | Required | Description |
|---|---|---|---|
prompt |
string | yes | Natural-language instruction, e.g. "extract all text in the image" |
image_path |
string | yes | Path to a local image (png/jpg/jpeg/gif/webp/bmp) |
Returns the model's text response.
Configuration
Required env
| Env | Purpose |
|---|---|
API_KEY |
Bearer token for the OpenAI-compatible API |
MODEL |
Model name, e.g. gpt-4o, glm-4v, etc. |
Optional env
| Env | Default | Purpose |
|---|---|---|
OPENAI_BASE_URL |
https://api.openai.com/v1 |
Base URL for any OpenAI-compatible endpoint |
Install
Option A: npx (once published to npm)
// e.g. Claude Code's mcp config
"image-recognition-mcp": {
"command": "npx",
"args": ["-y", "image-recognition-mcp"],
"env": {
"API_KEY": "your_key",
"OPENAI_BASE_URL": "https://api.openai.com/v1",
"MODEL": "gpt-4o"
}
}
Option B: local checkout
"image-recognition-mcp": {
"command": "node",
"args": ["/absolute/path/to/image_recognition_mcp/index.js"],
"env": {
"API_KEY": "your_key",
"OPENAI_BASE_URL": "https://api.openai.com/v1",
"MODEL": "gpt-4o"
}
}
Or after npm link in this repo:
"image-recognition-mcp": {
"command": "image-recognition-mcp",
"env": { "API_KEY": "...", "MODEL": "..." }
}
Self-test
API_KEY=test-key MODEL=gpt-4o node index.js --self-test
Verifies the image-file → data-URL helper (extension check + MIME map) without making an API call.
Notes
- Reads files from the local filesystem only. No URL fetching.
- Image is sent as a base64 data URL inside the
chat/completionsrequest body. Large images will produce large requests — resize before sending if your provider has size limits. - Errors from the VLM API are surfaced as tool-call errors (non-zero exit code on the tool result).
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