image-forensics-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 |
|---|---|
| noise_mapA | Extract the noise pattern from an image to help detect AI generation. Subtracts a Gaussian-blurred version from the original to isolate noise. AI-generated images tend to have unnaturally uniform or structured noise. Args: image_path: Absolute path to the image file. blur_sigma: Gaussian blur sigma (higher = more smoothing). Default 3.0. |
| error_level_analysisB | Perform multi-scale Error Level Analysis (ELA) on an image. Resaves at multiple JPEG quality levels and analyzes the error patterns. Includes consistency analysis across scales for better AI detection. Args: image_path: Absolute path to the image file. quality: Primary JPEG quality for visualization (1-100). Default 90. |
| frequency_analysisA | Perform FFT frequency analysis on an image. Analyzes the frequency spectrum for artifacts common in AI-generated images, including grid patterns, spectral peaks, and unusual energy distribution. Args: image_path: Absolute path to the image file. |
| spectral_decay_analysisA | Analyze the power spectral density decay curve of an image. Natural images follow a characteristic 1/f^beta power law. AI-generated images (especially from diffusion models) often show different decay slopes and anomalies in specific frequency bands. Based on CVPR 2025 research. Args: image_path: Absolute path to the image file. |
| color_analysisA | Analyze color channel correlations, saturation, and entropy for AI detection. Based on CVPR 2025 "Secret Lies in Color" — AI-generated images show unusual RGB channel correlations, gradient patterns, and saturation distributions compared to natural photographs. Args: image_path: Absolute path to the image file. |
| metadata_checkA | Check image metadata/EXIF for AI generation signatures and resolution fingerprints. Looks for known AI tool signatures, unusual metadata patterns, missing camera information, and AI-typical output resolutions. Args: image_path: Absolute path to the image file. |
| full_forensic_reportA | Run all forensic analyses and produce a comprehensive report with overall verdict. Combines noise map, ELA, frequency analysis, spectral decay, color analysis, and metadata check. Saves visualization images to /tmp/image-forensics/ and returns text-only results to avoid output overflow. Args: image_path: Absolute path to the image file. |
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 7 tools
Each tool targets a distinct forensic method (noise, ELA, FFT, spectral decay, color, metadata). However, frequency_analysis and spectral_decay_analysis both operate in the frequency domain and could be confused by an agent unfamiliar with the specific techniques. The descriptions clarify the differences, so overlap is limited.
Six of seven tools follow a consistent noun_analysis or noun_check pattern (e.g., noise_map, frequency_analysis, metadata_check). The outlier is full_forensic_report, which is a different type (aggregate report) but its name is still descriptive. Minor deviation from a strict pattern.
Seven tools is well-scoped for an image forensics server. Each tool provides a distinct analysis technique, and the inclusion of a full report tool adds convenience without redundancy. No tool feels unnecessary or missing.
The surface covers multiple orthogonal forensic techniques: noise, compression artifacts, frequency domain, spectral decay, color, metadata, and a comprehensive aggregator. This is complete for the stated purpose of AI image detection; no obvious gaps exist.