quelllm-mcp
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.
| Tool | Description |
|---|
list_models(filter_origin?, filter_family?, max_params_b?) | List models with filters (origin code, family, max params in B) |
get_model(model_id) | Full record for one model (params, vram per quant, context window, family, tags, license, URLs) |
compare(model_a_id, model_b_id) | Side-by-side comparison with verdict |
estimate_vram(model_id, quant) | VRAM in GB at chosen quant + recommended GPU/Mac tiers |
estimate_cost(input_tokens_per_month, output_tokens_per_month, ...) | Cost in EUR — full table API providers vs self-hosted hardware OR a specific id |
search_models(query, limit?) | Fuzzy search by name, family, tag, author |
Install
Install from source (not yet on PyPI) :
pip install git+https://github.com/MGM-FALCON/quelllm-mcp.git
Or run without installing, using uv :
uvx --from git+https://github.com/MGM-FALCON/quelllm-mcp.git quelllm-mcp
For local development :
git clone https://github.com/MGM-FALCON/quelllm-mcp.git
cd quelllm-mcp
pip install -e .
Use with Claude Code
Add to ~/.claude.json or a project's .mcp.json. If you installed with pip :
{
"mcpServers": {
"quelllm": {
"command": "quelllm-mcp"
}
}
}
Or zero-install with uvx :
{
"mcpServers": {
"quelllm": {
"command": "uvx",
"args": ["--from", "git+https://github.com/MGM-FALCON/quelllm-mcp.git", "quelllm-mcp"]
}
}
}
Use with Claude Desktop
Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) :
{
"mcpServers": {
"quelllm": {
"command": "quelllm-mcp"
}
}
}
Use with Cursor / Continue / Cline
Most MCP clients accept the same JSON config :
{
"command": "quelllm-mcp"
}
Example queries (from your client)
> Quels LLM Mistral peuvent tourner sur RTX 5070 Ti 16GB ?
→ list_models(filter_family='Mistral', max_params_b=24)
→ estimate_vram('mistral-small-24b', 'q4')
> Compare Llama 3.3 70B vs Qwen 2.5 32B
→ compare('llama33-70b', 'qwen25-32b')
> J'utilise 10M tokens input + 2.5M output / mois. Combien je paye chez OpenAI vs DeepSeek ?
→ estimate_cost(10_000_000, 2_500_000)
Data source
All data pulled from quelllm.fr/api/ (CC BY 4.0, no key, CORS-enabled). Cached locally for 1h to avoid rate-limiting.
API pricing data (GPT-5, Claude Opus 4.7, Gemini 2.5, DeepSeek, Mistral) and hardware pricing (RTX 50-series, Mac M4) are hardcoded as of 2026-05 — verify semestrially.
Model-selection guides by language
The MCP server exposes the same catalog to any compatible client. These companion guides explain model selection, memory trade-offs and repeatable evaluation tasks for different languages. They are source-based guides, not new hardware benchmarks.
License
MIT — see LICENSE.
Contributing
Source : https://github.com/MGM-FALCON/quelllm-mcp
Issues + PRs welcome. Particularly :
- API pricing updates (semestrial)
- Hardware additions (new GPUs, Mac Mx series)
- New tools (e.g.
find_alternatives_to(model_id), recommend_gpu(budget_eur))
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 on Python 3.9.
pip install -e ".[test]"
pytest
Author
Mohamed Meguedmi — LinkedIn · Hugging Face
Founder of La Gazette IA and QuelLLM.fr.