Iris — Evidence-first image reading for AI agents — metadata, OCR text, regions, and citeable evidence without generative LLM.
Image MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. Iris — Evidence-first image reading for AI agents — metadata, OCR text, regions, and citeable evidence without generative LLM.
Evidence-first image reading for AI agents. One call turns any local image into an Agent Media Twin — dimensions, metadata, optional OCR with bounding boxes, and trust warnings you can cite without asking a vision LLM to guess.
Once Image is connected, these are the calls the assistant has available:
Local-first — read_image resolves paths on the local machine; no cloud vision API by defaultDoc — Purposeread_image — Read a local image and return dimensions, mime, metadata, optional OCR, and trust warningsChannel — StatusInstallation goes through your MCP client rather than a global install: point it at @sylphx/image-reader-mcp on npm and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Image's toolset — Local-first, Doc, read_image and 1 more — is a fair guide to whether it matches your workflow. It is maintained by shtse8; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Image's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| Local-first | read_image resolves paths on the local machine; no cloud vision API by default. |
| Doc | Purpose |
| read_image | Read a local image and return dimensions, mime, metadata, optional OCR, and trust warnings. |
| Channel | Status |
{
"mcpServers": {
"image-reader-1": {
"command": "npx",
"args": ["-y", "@sylphx/image-reader-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.