The repo is based on Model Context procotol of Python SDK, including DL models in CV, and provide the abilities to the LLM or vLLM model
For CVDLT(Computer Vision & Deep Learning Tools) MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. The repo is based on Model Context procotol of Python SDK, including DL models in CV, and provide the abilities to the LLM or vLLM model.
Once For CVDLT(Computer Vision & Deep Learning Tools) is connected, these are the calls the assistant has available:
detect_objects — Detect objects in an image using YOLOv10Input — image_url (string)segment_objects — Segment objects in an image using YOLOv8segment_image — Segment entire image using Ultralytics SAMestimate_pose — Estimate human poses in an image using YOLOv8Resources — The Resources tool exposed by this serverTools — The Tools tool exposed by this serverSetup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. For CVDLT(Computer Vision & Deep Learning Tools)'s toolset — detect_objects, Input, segment_objects and 4 more — is a fair guide to whether it matches your workflow. It is maintained by MRonaldo-gif; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| detect_objects | Detect objects in an image using YOLOv10 |
| Input | image_url (string) |
| segment_objects | Segment objects in an image using YOLOv8 |
| segment_image | Segment entire image using Ultralytics SAM |
| estimate_pose | Estimate human poses in an image using YOLOv8 |
| Resources | The Resources tool exposed by this server. |
| Tools | The Tools tool exposed by this server. |
{
"mcpServers": {
"server-with-yolo": {
"url": "http://localhost:8080/sse"
}
}
}Configuration as documented by the project. Restart the client after saving.
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.