Schema | ai-vision-mcp

ai-vision-mcp

Describes the environment variables required to run the server.

Name	Required	Description
`GEMINI_API_KEY`	No	Your Google Gemini API key (required for Google provider)
`IMAGE_PROVIDER`	No	Provider for image analysis: 'google' or 'vertex_ai'
`VIDEO_PROVIDER`	No	Provider for video analysis: 'google' or 'vertex_ai'
`GCS_BUCKET_NAME`	No	Your Google Cloud Storage bucket name
`VERTEX_PROJECT_ID`	No	Your Google Cloud project ID
`VERTEX_PRIVATE_KEY`	No	Your Vertex AI private key
`VERTEX_CLIENT_EMAIL`	No	Your Vertex AI service account email

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.

Features and capabilities supported by this server

Protocol revision2025-11-25

Capability	Details
`tools`	{ "listChanged": true }
`logging`	{}

Functions exposed to the LLM to take actions

Name	Description
analyze_imageC	Analyze an image using AI vision models. Supports URLs, base64 data, and local file paths.
compare_imagesC	Compare multiple images using AI vision models. Supports URLs, base64 data, and local file paths.
detect_objects_in_imageB	Detect objects in an image using AI vision models and generate annotated images with bounding boxes. Supports URLs, base64 data, and local file paths. File handling: explicit filePath → exact path, otherwise → temp directory. Uses optimized default parameters for object detection.
analyze_videoB	Analyze a video using AI vision models. Supports URLs and local file paths.
audit_designB	Perform design compliance auditing with pixel-level analysis (K-means colors, Sobel edges, WCAG contrast) and the critique of Vision Language Model.

Interactive templates invoked by user choice

Name	Description
No prompts

Contextual data attached and managed by the client

Name	Description
No resources

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curl -X GET 'https://glama.ai/api/mcp/v1/servers/tan-yong-sheng/ai-vision-mcp'

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