io.github.prasadabhishek/photographi-mcp
PYPI · PHOTOGRAPHI-MCP · SCANNED SEP 21
Visual Intelligence Command Center: A Local Computer Vision Engine for Photo Libraries
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security49
- Malware scan not yet available for this package.Unverified
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs setuptools.build_meta at install time, a recognised native-build step with no shell scripting around it. View diagnostics → Pass
- 3 of 28 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency35
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- License check failed: the license (MIT License) isn't a recognized OSI-approved license. See how to fix → Fail
- Actively maintained (last published 217 days ago).Pass
- Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability62
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 1298 tokens (~162/item across 8 items; 8 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management87
- Stability observed for 26 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage92
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 73% of tool parameters carry a description.Partial
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 8 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 8 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
How do I install the io.github.prasadabhishek/photographi-mcp server?
io.github.prasadabhishek/photographi-mcp runs locally as a PyPI package, launched with uvx photographi-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · photographi-mcp
claude mcp add prasadabhishek-photographi-mcp -- uvx photographi-mcp
{
"mcpServers": {
"prasadabhishek-photographi-mcp": {
"command": "uvx",
"args": [
"photographi-mcp"
]
}
}
} {
"servers": {
"prasadabhishek-photographi-mcp": {
"command": "uvx",
"args": [
"photographi-mcp"
]
}
}
} codex mcp add prasadabhishek-photographi-mcp -- uvx photographi-mcp
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"prasadabhishek-photographi-mcp": {
"type": "local",
"command": [
"uvx",
"photographi-mcp"
],
"enabled": true
}
}
} openclaw mcp add prasadabhishek-photographi-mcp --command uvx --arg photographi-mcp
mcp_servers:
prasadabhishek-photographi-mcp:
command: "uvx"
args: ["photographi-mcp"] {
"McpServers": {
"prasadabhishek-photographi-mcp": {
"Transport": "stdio",
"Command": "uvx",
"Arguments": [
"photographi-mcp"
]
}
}
} assistant mcp add prasadabhishek-photographi-mcp -t stdio -c uvx -a photographi-mcp
{
"mcpServers": {
"prasadabhishek-photographi-mcp": {
"command": "uvx",
"args": [
"photographi-mcp"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 20 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 80 to 83. That category is still filling its 30-day observation window: 24 days of observed history at the previous scan, 25 at this one. The score rises as the window fills, whether or not the server changes.
- 19 Sept 26 −3
- Stability: pass → 0.80 functional
- 18 Sept 26 0
- Stability: 0.97 → pass security
- 17 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 93 to 97. That category is still filling its 30-day observation window: 28 days of observed history at the previous scan, 29 at this one. The score rises as the window fills, whether or not the server changes.
- 15 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 87 to 90. That category is still filling its 30-day observation window: 26 days of observed history at the previous scan, 27 at this one. The score rises as the window fills, whether or not the server changes.
- 13 Sept 26 −14
- Malware scan: pass → unverified ▼ security
- 12 Sept 26 +12
- Malware scan: unverified → pass ▲ security
- Stability: pass → 0.80 functional
- 11 Sept 26 0
- Stability: 0.97 → pass security
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 21 Sept 2026 · Analysed pypi/photographi-mcp@0.2.6
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | setuptools.build_meta |
Background: Why install scripts are a supply-chain risk →
Dependencies 28 packages
| Packages resolved | 28 |
|---|---|
| Stale | 2 |
| No linked repository | 1 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
photographi_analyze_folder Photographi Analyze Folder ~217
Batch analyzes all images in a folder with high concurrency (4-8 images at once). Returns quality scores and judgements for each image. Default limit is 100 images per call. Use 'limit' and 'offset' for pagination. If 'nextOffset' is in the response, call again with that offset. Perfect for understanding the overall quality distribution of a shoot.
| Name | Type | Req | Description |
|---|---|---|---|
| enable_subject_detection | boolean | – | Use AI for subject-aware analysis. |
| fast_mode | boolean | – | Defaults to True (4-8x faster). Set to False for full-resolution 'Forensic Precision' mode (much slower on 40MP+ files). |
| folder_path | string | yes | Absolute path to folder. |
| limit | integer | – | Batch size for pagination. |
| metrics | array | – | Specific metrics to calculate (sharpness, exposure, etc.). Defaults to all. |
| model_size | string | – | YOLO model size. |
| offset | integer | – | Pagination offset. Increment this by 'limit' to see more results. |
Structured output declared, but exposes no named fields.
No examples provided.
photographi_analyze_photo Photographi Analyze Photo ~164
Analyzes a single photo for technical quality (sharpness, exposure, noise, etc.). Returns overall score, judgement, and per-metric breakdowns. Use this for detailed inspection of individual images. For batch analysis, use photographi_analyze_folder.
| Name | Type | Req | Description |
|---|---|---|---|
| enable_subject_detection | boolean | – | – |
| fast_mode | boolean | – | Defaults to True (4-8x faster). Set to False for full-resolution 'Forensic Precision' mode (much slower on 40MP+ files). |
| image_path | string | yes | Absolute path to RAW/JPEG/TIFF. |
| metrics | array | – | Specific metrics (sharpness, exposure, noise, focus, color, dynamicRange, composition). |
| model_size | string | – | YOLO model size. |
Structured output declared, but exposes no named fields.
No examples provided.
photographi_cull_photographs Photographi Cull Photographs ~246
Culls low-quality images by moving them to a 'culled_photos' subfolder. Images ABOVE the threshold (default 0.4) stay in the original folder. Images BELOW the threshold are moved to culled_photos/. Processes 100 images per call by default. Use 'limit' and 'offset' for pagination. Mode options: 'move' (default), 'xmp' (sidecar tagging), or 'both'.
| Name | Type | Req | Description |
|---|---|---|---|
| enable_subject_detection | boolean | – | – |
| fast_mode | boolean | – | Defaults to True (4-8x faster). Set to False for full-resolution 'Forensic Precision' mode (much slower on 40MP+ files). |
| folder_path | string | yes | Absolute path to folder. |
| limit | integer | – | Number of images to process in this batch. |
| mode | string | – | Action to perform: 'move' (files to subfolder), 'xmp' (sidecar tags only), or 'both'. |
| offset | integer | – | Pagination offset. |
| threshold | number | – | Overall score threshold (0.0-1.0). Images below this are culled. |
Structured output declared, but exposes no named fields.
No examples provided.
photographi_get_color_palette Photographi Get Color Palette ~86
Extracts a representative color palette from a single image using K-Means clustering. Returns a list of hex color codes (e.g., ['#FF5733', '#33FF57', ...]). Default is 5 colors. Use this for design inspiration or color grading reference.
| Name | Type | Req | Description |
|---|---|---|---|
| colors | integer | – | – |
| image_path | string | yes | Absolute path to image. |
Structured output declared, but exposes no named fields.
No examples provided.
photographi_get_folder_palettes Photographi Get Folder Palettes ~95
Extracts color palettes from multiple images in a folder. Processes 20 images per call by default. Use 'limit' and 'offset' for pagination. Returns a map of filename → color palette. Useful for building color-based galleries or moodboards.
| Name | Type | Req | Description |
|---|---|---|---|
| colors | integer | – | – |
| folder_path | string | yes | Absolute path to folder. |
| limit | integer | – | – |
| offset | integer | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
photographi_get_scene_content Photographi Get Scene Content ~84
Detects objects in a photo using YOLO (e.g., person, dog, car, etc.). Returns a simple list of object labels. Use this for quick scene indexing or content-based search. For full quality analysis with subject-aware metering, use photographi_analyze_photo.
| Name | Type | Req | Description |
|---|---|---|---|
| image_path | string | yes | Absolute path to RAW/JPEG/TIFF. |
Structured output declared, but exposes no named fields.
No examples provided.
photographi_rank_photographs Photographi Rank Photographs ~193
Ranks photos by technical quality and returns the top_n best images. Uses high concurrency to process up to 100 images per call (default limit). Ideal for burst sequences or finding hero shots. Returns detailed metrics for each top image. For full folder analysis, use photographi_analyze_folder instead.
| Name | Type | Req | Description |
|---|---|---|---|
| enable_subject_detection | boolean | – | – |
| fast_mode | boolean | – | Defaults to True (4-8x faster). Set to False for full-resolution 'Forensic Precision' mode (much slower on 40MP+ files). |
| folder_path | string | yes | Absolute path to folder. |
| limit | integer | – | Max images to evaluate in this batch. |
| metrics | array | – | Specific metrics for ranking. |
| model_size | string | – | YOLO model size. |
| offset | integer | – | Pagination offset. |
| top_n | integer | – | Number of top-rated images to return. |
Structured output declared, but exposes no named fields.
No examples provided.
photographi_threshold_cull Photographi Threshold Cull ~213
Binary threshold culling: moves images below min_confidence (default 0.6) to a 'rejects' subfolder. Images ABOVE the threshold stay in the original folder. Similar to photographi_cull_photographs, but uses a higher default threshold and 'rejects/' folder name for stricter filtering. Processes 100 images per call. Mode options: 'move', 'xmp', or 'both'.
| Name | Type | Req | Description |
|---|---|---|---|
| enable_subject_detection | boolean | – | – |
| fast_mode | boolean | – | Defaults to True (4-8x faster). Set to False for full-resolution 'Forensic Precision' mode (much slower on 40MP+ files). |
| folder_path | string | yes | Absolute path to folder. |
| limit | integer | – | – |
| min_confidence | number | – | – |
| mode | string | – | Action to perform: 'move' (files to subfolder), 'xmp' (sidecar tags only), or 'both'. |
| offset | integer | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
What is the io.github.prasadabhishek/photographi-mcp server?
io.github.prasadabhishek/photographi-mcp is listed in the public MCP registry as io.github.prasadabhishek/photographi-mcp. Visual Intelligence Command Center: A Local Computer Vision Engine for Photo Libraries. This page covers its PyPI package (photographi-mcp).
Is the io.github.prasadabhishek/photographi-mcp server safe to use?
io.github.prasadabhishek/photographi-mcp scores 62 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 21 September 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.prasadabhishek/photographi-mcp server expose?
io.github.prasadabhishek/photographi-mcp exposes 8 tools: photographi_analyze_photo, photographi_analyze_folder, photographi_rank_photographs, photographi_cull_photographs, photographi_threshold_cull, and 3 more. Their descriptions and schemas cost roughly 1,298 tokens of context every time the server is loaded.
Is the io.github.prasadabhishek/photographi-mcp server still maintained?
io.github.prasadabhishek/photographi-mcp is still listed as active in the MCP registry. We last reached this channel on 21 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.