Iris vs AI Vision MCP
A side-by-side look at the Iris and AI Vision MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Iris for multimedia process work as a local process; choose AI Vision MCP for multimedia process as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Iris when
- You need dedicated capabilities in the Multimedia Process domain.
- You prefer a local stdio subprocess transport.
- You want tools like
read_image, image_probe, crop_region.
Choose AI Vision MCP when
- You need dedicated capabilities in the Multimedia Process domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You have the keys it needs:
IMAGE_PROVIDER, VIDEO_PROVIDER, GEMINI_API_KEY, VERTEX_CLIENT_EMAIL, VERTEX_PRIVATE_KEY, VERTEX_PROJECT_ID, GCS_BUCKET_NAME. - You want tools like
analyze_image, compare_images, detect_objects_in_image.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Multimedia Process | Multimedia Process |
| Quality signal | 40/100 · Fair | 63/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | API key required |
| Pricing | Not declared | BYOK (pay the provider) |
| Required env vars | None listed | IMAGE_PROVIDERVIDEO_PROVIDERGEMINI_API_KEYVERTEX_CLIENT_EMAILVERTEX_PRIVATE_KEYVERTEX_PROJECT_IDGCS_BUCKET_NAME |
| Verified owner | Community listing | Community listing |
| GitHub stars | 2 | 80 |
| Directory activity | 1 views 0 installs 0 upvotes | 5 views 0 installs 0 upvotes |
| Tools | 4 | 4 |
| Full listing | View Iris | View AI Vision MCP |
Tools & capabilities
Iris tools 4
read_imageOne local image. Geometry and metadata. OCR only when requested. An optional `region` crops inside this read.image_probeFormat, dimensions, pixel count, and source hash. No OCR and no crop.crop_regionOne citeable region: bounds and hash. Set `include_region_image` for PNG bytes. No OCR.compare_imagesChanged pixels between two images of equal dimensions. No OCR.
AI Vision MCP tools 4
analyze_imageAnalyze an image using AI vision models. Supports URLs, base64 data, and local file paths.compare_imagesCompare multiple images using AI vision models. Supports URLs, base64 data, and local file paths.detect_objects_in_imageDetect objects in an image using AI vision models and generate annotated images with bounding boxes. Supports URLs, base64 data, and local file paths. File handling: explicit filePath → exact path, otherwise → temp directory. Uses optimized default parameters for object detection.analyze_videoAnalyze a video using AI vision models. Supports URLs and local file paths.
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Iris
{
"mcpServers": {
"iris-2": {
"command": "npx",
"args": [
"-y",
"@sylphx/anymd"
]
}
}
}
AI Vision MCP
{
"mcpServers": {
"tan-yong-sheng-ai-vision-mcp": {
"command": "npx",
"args": [
"-y",
"ai-vision-mcp"
],
"env": {
"IMAGE_PROVIDER": "YOUR_IMAGE_PROVIDER_HERE",
"VIDEO_PROVIDER": "YOUR_VIDEO_PROVIDER_HERE",
"GEMINI_API_KEY": "YOUR_GEMINI_API_KEY_HERE",
"VERTEX_CLIENT_EMAIL": "YOUR_VERTEX_CLIENT_EMAIL_HERE",
"VERTEX_PRIVATE_KEY": "YOUR_VERTEX_PRIVATE_KEY_HERE",
"VERTEX_PROJECT_ID": "YOUR_VERTEX_PROJECT_ID_HERE",
"GCS_BUCKET_NAME": "YOUR_GCS_BUCKET_NAME_HERE"
}
}
}
}
Frequently asked questions
Iris is categorized under Multimedia Process and uses a local stdio subprocess. AI Vision MCP belongs to Multimedia Process and uses a local stdio subprocess. Pick Iris for multimedia process work and AI Vision MCP for multimedia process.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Iris has no declared pricing — check the project's own site. AI Vision MCP is bring-your-own-key: usage is billed by the API provider you supply a key for. Auth: api key required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add iris-2 and tan-yong-sheng-ai-vision-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Iris has a quality signal of 40/100 (Fair) with 1 views, 0 installs and 2 GitHub stars. AI Vision MCP has 63/100 (Good) with 5 views, 0 installs and 80 GitHub stars.