`photographi-mcp` is an MCP server that enables AI models and LLM-powered tools to perform technical analysis on local photo libraries. It runs
Photographi MCP MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. photographi-mcp is an MCP server that enables AI models and LLM-powered tools to perform technical analysis on local photo libraries. It runs computer vision models directly on your hardware (powered by.
Once Photographi MCP is connected, these are the calls the assistant has available:
analyze_photo — "Is this dog photo sharp enough for a print?"analyze_folder — "How's the overall quality of my 'Vacation' folder?"rank_photographs — "Find the best shot in this burst of the cake."cull_photographs — "Move all the blurry photos to a junk folder."threshold_cull — "Strictly separate keepers using a score of 0.7."get_color_palette — "What colors are in this sunset for my website?"get_folder_palettes — "Generate a moodboard from my 'Forest' shoot."get_scene_content — "Which photos contain a 'cat' or 'mountain'?"The server ships on PyPI as photographi-mcp, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Photographi MCP's toolset — analyze_photo, analyze_folder, rank_photographs and 5 more — is a fair guide to whether it matches your workflow. It is maintained by prasadabhishek; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| analyze_photo | "Is this dog photo sharp enough for a print?" |
| analyze_folder | "How's the overall quality of my 'Vacation' folder?" |
| rank_photographs | "Find the best shot in this burst of the cake." |
| cull_photographs | "Move all the blurry photos to a junk folder." |
| threshold_cull | "Strictly separate keepers using a score of 0.7." |
| get_color_palette | "What colors are in this sunset for my website?" |
| get_folder_palettes | "Generate a moodboard from my 'Forest' shoot." |
| get_scene_content | "Which photos contain a 'cat' or 'mountain'?" |
{
"mcpServers": {
"photographi": {
"command": "uvx",
"args": ["photographi-mcp"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.