MCP server for quelllm.fr: 190+ open-weights LLM catalog - list, compare, VRAM and cost estimates
Most AI and media services work still happens through a UI a human drives. Quelllm MCP server moves it into the conversation instead. MCP server for quelllm.fr: 190+ open-weights LLM catalog - list, compare, VRAM and cost estimates.
MCP server exposing the quelllm.fr catalog of 190+ open-weights LLMs via Model Context Protocol tools. Use it from Claude Code, Cursor, Continue, or any MCP-compatible client to query models, compare them, estimate VRAM, and compute API vs self-hosted cost.
The server publishes 1 tool. What each one is for:
Tests — A pytest smoke suite lives under tests/. It covers all 6 tools and the v1.1.0 output invariants, never touches the network (local fixture + mockedBecause this one is hosted, setup is mostly authentication — you point your client at the endpoint and approve access. Nothing runs on your machine, so there is no runtime to keep patched.
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. Quelllm's toolset — Tests — is a fair guide to whether it matches your workflow. It is maintained by MGM-FALCON; 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We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.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 |
|---|---|
| Tests | A pytest smoke suite lives under tests/. It covers all 6 tools and the v1.1.0 output invariants, never touches the network (local fixture + mocked httpx), and stubs the mcp SDK when it isn't importable — so it also runs |
{
"mcpServers": {
"quelllm": {
"command": "quelllm-mcp"
}
}
}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.