imagefeatures-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 |
|---|---|
| analyze_imageA | Analyze an image's visual composition using mathematical features. This extracts statistical properties (color distribution, texture patterns, edge orientations) that describe HOW an image looks, not WHAT it contains. Args: image_path: Path to the image file (jpg, png, webp, etc.) preset: Analysis depth - one of: - "quick": Color histogram + edges (144 dims, fast) - "color": Detailed color analysis (605 dims) - "color_advanced": Fuzzy, scalable, correlogram features - "texture": LBP, Tamura, Haralick, Gabor (328 dims) - "texture_advanced": Rotation-invariant LBP, Centrist - "shape": Edge histogram, HOG, Hu moments (231 dims) - "shape_advanced": PHOG pyramid (630 dims) - "layout": Spatial color/luminance layout (76 dims) - "combined": CEDD, FCTH, JCD - best for similarity (504 dims) - "full": All 22 features (3058 dims, comprehensive) Returns: Human-readable analysis with feature interpretations. |
| compare_imagesA | Compare two images mathematically to get a similarity score. This measures visual similarity based on mathematical features, NOT semantic content. Different subjects can be "similar" if they share color palettes, textures, or compositions. Args: image_a: Path to first image image_b: Path to second image feature: Feature for comparison. Recommended: - "CEDD": Color + edge (144 dims, good general purpose) - "JCD": Joint CEDD+FCTH (168 dims, best for similarity) - "ColorHistogram": Color only (64 dims) - "LocalBinaryPatterns": Texture only (256 dims) - "PHOG": Shape only (630 dims) metric: Distance metric - "cosine", "euclidean", "l1" Returns: Similarity score (0-100%) and interpretation. |
| find_similar_in_folderA | Find visually similar images in a folder. Scans all images and ranks them by visual similarity to the query. Similarity is based on mathematical features, not semantic content. Args: query_image: Path to the reference image folder_path: Folder to search top_k: Number of results (1-20) feature: Feature for comparison - "JCD", "CEDD", "ColorHistogram", etc. Returns: Ranked list of similar images with scores. |
| get_dominant_colorsA | Extract the dominant colors from an image. Uses K-means clustering to find the most prominent colors. Returns exact hex codes and percentages. Args: image_path: Path to the image num_colors: Number of colors to return (1-5) Returns: List of hex codes with percentages. |
| check_image_qualityA | Analyze image quality: blur, contrast, texture complexity. Uses texture analysis to detect blur and quality issues. More reliable than asking an LLM to visually judge blur. Args: image_path: Path to the image Returns: Quality assessment with specific metrics. |
| sort_by_colorA | Sort all images in a folder by their dominant hue. Returns images ordered: grayscale → warm (red/orange/yellow) → green → cool (blue/cyan) → purple → back to red. Useful for creating color-organized galleries. Args: folder_path: Folder containing images Returns: Ordered list of images with dominant hue values. |
| filter_by_vibeA | Filter images by visual "vibe" categories. Uses color and texture features to categorize images into semantic groups based on their mathematical properties. Args: folder_path: Folder to search vibe: Category to filter by: - "blue_water": Blue dominant, water/sky scenes - "green_nature": Green dominant, nature scenes - "warm_sunset": Orange/red/yellow tones - "cool_moody": Blue/purple, low saturation - "high_contrast": Strong texture, high contrast - "soft_minimal": Low texture, smooth gradients - "grayscale": Black and white or desaturated - "vibrant": High saturation, colorful Returns: List of matching images with confidence scores. |
| extract_featuresA | Extract raw feature vectors from an image. Returns numerical vectors for custom ML pipelines, database indexing, or advanced analysis. Args: image_path: Path to the image features: Comma-separated feature names. Available (22 total): Color: ColorHistogram, ColorMoments, OpponentHistogram, FuzzyColorHistogram, DominantColors, ScalableColor Texture: LocalBinaryPatterns, RotationInvariantLBP, Gabor, Tamura, Haralick, Centrist Shape: EdgeHistogram, PHOG, HOG, HuMoments Layout: ColorLayout, LuminanceLayout Combined: CEDD, FCTH, JCD, AutoColorCorrelogram Returns: JSON with feature vectors and statistics. |
| list_featuresA | List all available features from the imagefeatures library. Returns documentation for all 22 feature extractors with dimensions and recommended use cases. |
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 9 tools
Each tool has a clearly distinct purpose: analysis, quality check, comparison, raw feature extraction, vibe filtering, similarity search, color extraction, feature listing, and color sorting. Even overlapping areas like comparison and similarity are differentiated by scope (two images vs. a folder).
All tool names follow a consistent snake_case verb_noun pattern (e.g., analyze_image, sort_by_color, get_dominant_colors). There are no mixed conventions or vague verbs like 'process' or 'do_thing'.
9 tools is appropriate for the domain of mathematical image feature extraction. Each tool provides a distinct operation without unnecessary duplication or missing essential functionality.
The tool set covers the full lifecycle of feature-based image analysis: single image analysis, quality assessment, pairwise comparison, raw feature extraction, vibe categorization, similarity search, color extraction, feature enumeration, and folder organization. No obvious gaps for the stated purpose.