Free2AITools — AI Model Discovery API & MCP Server
Description
# Free2AITools — The Open-Source AI Registry Discover, rank, and compare AI models, datasets, papers, tools, and benchmarks. Updated daily, scored by the Free2AITools Nexus Index (FNI). **Website**: [free2aitools.com](https://free2aitools.com) ## What It Does - **Cross-source…
About
# Free2AITools — The Open-Source AI Registry Discover, rank, and compare AI models, datasets, papers, tools, and benchmarks. Updated daily, scored by the Free2AITools Nexus Index (FNI). **Website**: [free2aitools.com](https://free2aitools.com) ## What It Does - **Cross-source catalog** — Models, datasets, papers…
Details
- Author
- mosesy5688-cell
- GitHub stars
- 1
- Downloads
- 268
- Categories
- Search, AI, Other, Developer Tools
Jump to
- Cross-source catalog from multiple AI platforms
- FNI ranking with full factor breakdown per entry
- Hardware-aware filtering by VRAM, license, and task
- Daily automated data refresh pipeline
- MIT licensed and free to use
- Exports available as open Parquet files
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Free2AITools — AI Model Discovery API & MCP ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Send HTTP requests to the API endpoints (/api/v1/select, /api/v1/compare, /api/v1/search) or add the MCP server to MCP-compatible clients (Claude, Cursor, Windsurf) by specifying "url": "https://free2aitools.com/api/mcp". No authentication required.
free2aitools_search
Keyword discovery over the Free2AITools catalog of AI models, datasets, papers, and tools. Returns matching catalog entries (metadata) ranked by FNI (Free2AITools Nexus Index), a 5-factor score: Semantic relevance, Authority, Popularity, Recency, Quality. The Semantic factor is a query-time baseline, not a live per-entity measurement (fni_s is returned null with a note). USE WHEN you need to discover which AI entities exist for a topic or keyword. DO NOT USE for general web search, to run/call/execute a model, to get a generated or inferred answer, or to route to an inference provider — this returns catalog metadata only, for the calling agent to reason over and decide on. Free discovery catalog: results are FNI-ranked, never paid placement / sponsored, and there is no billing or payment. Read-only, no side effects. May return a retryable transient 503 under cold-path or fallback budget limits; retry according to Retry-After. Use free2aitools_select_model instead when you have specific hardware or license constraints.
free2aitools_rank
Keyword-search AI entities using the task/query text as input and return FNI-ranked catalog entries. Mechanically this is the same keyword search as free2aitools_search with the task text folded into the query; it does NOT perform task-fit recommendation, compatibility analysis, model inference, or model execution, and it is NOT an inference router. USE WHEN you have task text and want catalog entries ordered by FNI. The caller makes the final selection; results are never paid placement and there is no billing. Read-only, no side effects. May return a retryable transient 503 under cold-path or fallback budget limits; retry according to Retry-After. Use free2aitools_search for plain keyword discovery, or free2aitools_select_model to apply hardware/license metadata filters.
free2aitools_explain
Explain why one specific entity received its FNI score, returning the 5-factor breakdown: Semantic (S), Authority (A), Popularity (P), Recency (R), Quality (Q). FNI = 0.35*S + 0.25*A + 0.15*P + 0.15*R + 0.10*Q (the S factor is a baseline, surfaced with a caveat, not a measured per-entity value). USE WHEN you already have one entity id (from a search/rank/select result) and want its score rationale. DO NOT USE to search/discover entities, to run a model, or to get a recommendation — this only describes scoring evidence for the caller to interpret. Read-only, no side effects, no billing. Use free2aitools_compare instead for side-by-side differences across multiple entities.
free2aitools_select_model
Filter the Free2AITools catalog by declared hardware/license metadata and return FNI-ranked candidate entries. USE WHEN you have concrete constraints (VRAM, params, license, context length, local-runnability) and want candidates narrowed by them. Constraints are metadata/heuristic filters over stored fields, NOT verified compatibility analysis, model inference, or model execution; this tool does not decide for you and is not an inference router. The caller is responsible for the final selection. Results are FNI-ranked, never paid placement, with no billing. Read-only, no side effects. Use free2aitools_search for unconstrained keyword discovery, or free2aitools_rank for keyword ranking without metadata filters.
free2aitools_compare
Compare 2-25 AI catalog entities side-by-side — any catalog entity type (models, datasets, papers, tools), not models only — showing FNI scores, factor breakdown (Semantic, Authority, Popularity, Recency, Quality), specs (params, VRAM, context length) where applicable, and license. USE WHEN you already have 2+ specific entity ids and want a structured side-by-side. DO NOT USE to discover entities, to run/execute a model, or to get a recommendation; the tool presents comparison facts for the caller to decide on, is not an inference router, and returns no paid placement. Read-only, no side effects, no billing. Cold upper-range multi-paper requests may return a transient 503 (retry after the indicated delay). Use free2aitools_select_model or free2aitools_search to discover candidates first, then compare the top ones.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"free2aitools \u2014 ai model discovery api & mcp server": {
"free2aitools": {
"url": "https://free2aitools.com/api/mcp",
"transport": "streamable-http"
}
}
}
}McpServers
{
"free2aitools": {
"url": "https://free2aitools.com/api/mcp",
"transport": "streamable-http"
}
}Free2AITools — The Open-Source AI Registry
Discover, rank, and compare AI models, datasets, papers, tools, and benchmarks. Updated daily, scored by the Free2AITools Nexus Index (FNI).
- Cross-source catalog— Models, datasets, papers, tools, and benchmarks from HuggingFace, GitHub, ArXiv, Ollama, Replicate, Civitai, and more
- FNI Ranking— 5-factor composite score (Semantic, Authority, Popularity, Recency, Quality) with full breakdown in every API response
- Hardware-aware filtering— filter models by VRAM, license, and task-category metadata (heuristic filters, not verified compatibility)
- Daily Updates— Automated pipeline refreshes all data daily
curl -s https://free2aitools.com/api/v1/select \ -H "Content-Type: application/json" \ -d '{"task":"text-generation","constraints":{"max_vram_gb":8}}'
Returns FNI-ranked catalog entries with params, VRAM estimates, license, and a factual FNI factor summary (fni_summary) — not a fit verdict; the caller decides.
# <ID_1>,<ID_2> are id values taken from /api/v1/search results — substitute your own. curl "https://free2aitools.com/api/v1/compare?ids=<ID_1>,<ID_2>"
Side-by-side comparison with FNI factor decomposition. Obtain ids from the Search API first; see theDeveloper Docsfor a runnable search-first flow.
curl "https://free2aitools.com/api/v1/search?q=code+generation&limit=5"
Add to Claude, Cursor, Windsurf, or any MCP-compatible client:
{ "mcpServers": { "free2aitools": { "url": "https://free2aitools.com/api/mcp" } } }
5 tools:free2aitools_search,free2aitools_rank,free2aitools_explain,free2aitools_select_model,free2aitools_compare.
Also listed on theSmithery directory listing.
Embed a live FNI score in your README. Replace<YOUR_UMID>with theidof your entity from a Search API response (this is a template, not a live badge):
!FNI Score
Parquet exports available for offline analysis with DuckDB, Pandas, or Spark.
- Developer Docs— API reference, MCP setup, Badge integration
- FNI Methodology— Scoring formula, sub-factors, anti-manipulation
- Trends— Weekly AI model intelligence
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