list_modelsLABSCO SUMMARY
Hardware and price answers, not just a model list.
The catalogue is the input; the value is the arithmetic on top — VRAM at a given quant against real GPU tiers, and an API-versus-self-hosted cost table in EUR. One caveat matters for the cost side: the API and hardware prices are hardcoded as of 2026-05 and the README asks for them to be verified semestrially, so treat the VRAM answer as durable and the euro figures as a snapshot.
What it is
An MCP server over the quelllm.fr catalogue of 190+ open-weights LLMs. It turns the catalogue into six tools a client can call: filter and search models, read one model's full record, compare two of them, size the VRAM at a chosen quantisation, and price API usage against self-hosting.
What you get
- Models filtered by origin, family or maximum parameter count —
list_models - One model's full record: params, VRAM per quant, context window, family, tags, license and URLs —
get_model - A side-by-side comparison of two models with a verdict —
compare - VRAM in GB at a chosen quantisation, plus the GPU and Mac tiers that fit —
estimate_vram - A monthly cost table in EUR comparing API providers against self-hosted hardware —
estimate_cost - Fuzzy search across name, family, tag and author —
search_models
Requirements
No key. Install from source with pip install git+https://github.com/MGM-FALCON/quelllm-mcp.git, or run it without installing via uvx --from git+https://github.com/MGM-FALCON/quelllm-mcp.git quelllm-mcp — so uv or pip plus network access is the whole setup. Catalogue data comes from the public quelllm.fr API (CC BY 4.0, no key) and is cached locally for 1h. The version is 1.1.0.
Setup effort
One command — pip install git+https://github.com/MGM-FALCON/quelllm-mcp.git
USE CASES
01
Checking whether a model fits your GPU
Ask for the VRAM at the quantisation you would actually run and get the GPU and Mac tiers that can hold it.
02
Choosing between two open-weights models
Put them side by side on params, context window and license rather than reading two model cards.
03
Deciding between an API and your own hardware
Give your monthly input and output token volumes and read the cost table both ways.
04
Shortlisting models under a size limit
Filter by family and maximum parameter count, or search by a half-remembered name.
6 tools
Listed in project docs. Your client may show a different list.
get_modelcompareestimate_vramestimate_costsearch_modelsBEFORE YOU INSTALL
What to know first
Tools you get
6 tools named in the project's documentation.
Requires
quelllm-mcp on PATH
Where it runs
All 5 documented clients run the server on your machine.
INSTALLATION
Connect your client
Server setup
TERMINAL
pip install git+https://github.com/MGM-FALCON/quelllm-mcp.gitMCP Clients
Claude Code
CLAUDE CODE · JSON
{
"mcpServers": {
"quelllm": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/MGM-FALCON/quelllm-mcp.git",
"quelllm-mcp"
]
}
}
}Claude Desktop
CLAUDE DESKTOP · JSON
{
"mcpServers": {
"quelllm": {
"command": "quelllm-mcp"
}
}
}Cursor, Continue, Cline
JSON
{
"mcpServers": {
"quelllm-mcp": {
"command": "quelllm-mcp"
}
}
}
