PicDefense.io MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PORT | No | Server listen port | 6910 |
| PICDEFENSE_API_TOKEN | Yes | Your PicDefense API token in the format USERID:APIKEY | |
| PICDEFENSE_API_BASE_URL | No | API base URL | https://app.picdefense.io/api/v2 |
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": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| picdefense_get_creditsA | Get the remaining credit balance on your PicDefense.io account. Most analysis tools consume credits per call — check this first if calls start failing with "insufficient credits". |
| picdefense_check_image_riskA | Run PicDefense's core image-risk analysis on an image URL. Returns reverse-image-search findings and a "picrisk" score indicating how widely / riskily the image appears online. This is the primary tool for assessing whether an image has been misused or scraped. Consumes account credits. |
| picdefense_extract_exifA | Extract EXIF metadata (camera, timestamps, GPS, etc.) embedded in an image at the given URL. Consumes account credits. |
| picdefense_detect_faceA | Detect whether an image contains a human face. Consumes account credits. |
| picdefense_detect_landmarkA | Detect whether an image contains a recognizable landmark (and where). Consumes account credits. |
| picdefense_detect_logoA | Detect whether an image contains a brand logo. Consumes account credits. |
| picdefense_safesearchA | Assess the content safety of an image (adult, violence, racy, medical, spoof likelihoods) via SafeSearch. Consumes account credits. |
| picdefense_find_backlinksA | Find backlinks for an image — other web pages where the image appears. Useful for tracing where a photo has been republished. Consumes account credits. |
| picdefense_detect_labelsA | Detect descriptive labels for the contents of an image (objects, scenes, concepts). Consumes account credits. |
| picdefense_extract_textA | Extract text from an image via OCR. Returns the detected full text, individual words, and a "truncated" flag (text is capped for very text-dense images). Consumes account credits. |
| picdefense_detect_watermarkA | Detect a visible stock/photographer watermark in an image. Returns whether one was detected, a confidence score, and the identified source (e.g. a stock agency, "photographer", or "copyright"). Consumes account credits. |
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 11 tools
Each tool targets a distinct aspect of image analysis: risk, EXIF, faces, landmarks, logos, content safety, backlinks, labels, OCR, watermark, and credits. The descriptions clearly delineate overlapping concepts like reverse-image-search findings vs. dedicated detection tools, so an agent can unambiguously select the right one.
All tool names follow the 'picdefense_<verb_noun>' pattern (e.g., extract_exif, detect_face, find_backlinks) with a consistent prefix, but 'safesearch' deviates as a single compound noun rather than a verb_noun construction. This minor inconsistency does not hinder readability.
With 11 tools, the server achieves a well-scoped balance—enough to cover the full spectrum of image risk and detection features without overwhelming the agent. Each tool earns its place and there is no apparent bloat.
The tool surface comprehensively covers the image-risk domain: credit management, core risk scoring, EXIF, face/landmark/logo/watermark detection, content safety, backlinks, labels, and OCR. No obvious lifecycle or analytical gaps exist for typical use cases.