Analyze an image using a vision-language model. Returns a unified JSON envelope wrapping summary, observations, uncertainties, and suggested follow-ups (see README 'Response format' for the full schema).

Supports URL, local file path, data URL, and Base64 input.

Task types guide the model:

• general: General analysis (default)

• qa: Answer a specific question about the image

• ui: Analyze UI/layout/interactions/accessibility

• chart: Analyze charts/graphs/data

• document: OCR and document structure

• object: Identify and locate objects

• screenshot: Analyze application screenshots

• code_screenshot: Read code from screenshots

Inspect image metadata (dimensions, format, size, mode) without calling VLM. Use this before detailed analysis to understand the image dimensions and plan crop coordinates.

Crop a region of an image and analyze it with VLM. This is the most powerful tool for inspecting small text, UI elements, chart data, or error messages.

Coordinates are NORMALIZED (0.0 to 1.0), where (0,0) is top-left and (1,1) is bottom-right.

Workflow: Use vision_inspect first to get dimensions, then vision_analyze for overview, then vision_crop_analyze to zoom into specific regions of interest.

Extract visible text from an image using OCR. Returns structured text organized by reading order.

Use this for: screenshots with text, scanned documents, receipts, tables, forms, Chinese/English OCR, and any text-heavy images.

Uses a configured dedicated OCR model when enabled. If the dedicated OCR model is unavailable, automatically falls back to the VLM provider.

Compare two or more images and identify differences. Use for:

• UI regression testing (before/after screenshots)

• Design vs implementation comparison

• Bug screenshot comparison

• Version diff of documents

Returns structured differences with confidence levels.

Return current vision-mcp server capabilities, supported models, and limits. Call this to discover what the server can do before using other tools.