ollama-vision-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| VISION_MODEL | No | Model name in Ollama (must have vision capability) | gemma4 |
| OLLAMA_BASE_URL | No | Ollama API endpoint | http://127.0.0.1:11434 |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| describe_imageA | Describe the content of an image file. Args: image_path: Absolute path to the image file (png, jpg, webp, gif) detail_level: Level of detail - "brief", "detailed", or "exhaustive" |
| ocr_imageA | Extract all visible text from an image (OCR). Args: image_path: Absolute path to the image file language: Primary language hint - "auto", "vi", "en", "ja", "zh", "ko" |
| ask_imageB | Ask any question about an image. Args: image_path: Absolute path to the image file question: Your question or prompt about the image |
| process_clipboard_imageA | Process an image from the macOS clipboard. Call this when user pastes an image or sends [Image] without a file path. IMPORTANT: Call this tool whenever you see [Image 1], [Image 2], or the user pastes an image from clipboard. The primary model has no vision capability — this tool reads the image from clipboard and analyzes it. Args: task: Processing type - "describe" (describe image), "ocr" (extract text), or any custom question about the image |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
describe_image and ask_image both handle arbitrary image analysis, and process_clipboard_image explicitly supports describe/ocr/custom-question tasks, creating overlap. However, the input-source distinction (file vs clipboard) reduces ambiguity, and ocr_image is clearly distinct.
Three tools follow a consistent verb_noun pattern (describe_image, ocr_image, ask_image), and process_clipboard_image also uses verb_noun but adds a modifier. The naming is mostly consistent and predictable.
4 tools is a reasonable size for a vision-focused server, covering core operations without being bloated. It could have been higher if not for the redundancy between clipboard and file-based tools.
The toolset covers the main vision tasks (description, OCR, custom Q&A) and handles both file and clipboard inputs. Minor gaps like batch processing or image comparison are not essential for the stated purpose.