free2aitools_searchLABSCO SUMMARY
It scores, and it shows its working.
Returning the five factors alongside the total is the design choice that makes the ranking usable — you can see whether a model is at the top because it is genuinely strong or because it was released last week. The hardware filters are described by the project itself as heuristic rather than verified compatibility, which is the right caveat to carry into any 'will this fit in 8GB' answer: treat the VRAM estimate as a shortlist, not a guarantee.
What it is
A hosted MCP server over the Free2AITools registry, a cross-source catalog of AI models, datasets, papers, tools and benchmarks. Entries are scored by a composite index built from five factors — Semantic, Authority, Popularity, Recency and Quality — and every API response carries the full factor breakdown rather than only the total.
What you get
- The catalog searched across sources including HuggingFace, GitHub, ArXiv, Ollama, Replicate and Civitai —
free2aitools_search - Results ranked by the composite index —
free2aitools_rank - The reasoning behind a score, factor by factor —
free2aitools_explain - Model selection under constraints such as a VRAM ceiling and a task category, returning ranked entries with parameters, VRAM estimates and license —
free2aitools_select_model - Two or more entries compared side by side with their factor decomposition —
free2aitools_compare - A factual factor summary rather than a verdict: the response reports what the index says and leaves the fit judgement to the caller
Requirements
Nothing — no auth required and free to use. Add "url": "https://free2aitools.com/api/mcp" to your client's server list and it works in Claude, Cursor, Windsurf or any MCP-compatible client. The same catalog is also reachable as plain HTTP endpoints (/api/v1/search, /api/v1/select, /api/v1/compare) and as Parquet exports for offline analysis with DuckDB, Pandas or Spark. The repository package is ai-nexus (2.1.0), MIT licensed.
Setup effort
Paste a URL — add the endpoint to your client
USE CASES
Finding a model that fits the hardware you have
Ask for a task category with a VRAM ceiling and get ranked candidates with parameter counts, VRAM estimates and licenses.
Choosing between two shortlisted models
Compare them head to head and read the factor decomposition rather than a single number.
Understanding why something ranks where it does
Ask for the explanation and see which of the five factors is carrying the score.
Surveying what exists for a task
Search across model hubs, code and papers in one query instead of checking each source separately.
5 tools
Listed in project docs. Your client may show a different list.
free2aitools_rankfree2aitools_explainfree2aitools_select_modelfree2aitools_compareBEFORE YOU INSTALL
What to know first
Tools you get
5 tools named in the project's documentation.
Requires
A client that can reach a remote MCP server over HTTP
Where it runs
Both documented clients connect to a hosted endpoint.
INSTALLATION
Connect your client
Add the endpoint to your client.
Server setup
https://free2aitools.com/api/mcpMCP Clients
Cursor
{
"mcpServers": {
"free2aitools": {
"url": "https://free2aitools.com/api/mcp"
}
}
}Windsurf
{
"mcpServers": {
"free2aitools": {
"serverUrl": "https://free2aitools.com/api/mcp"
}
}
}