Search, rank, and compare 500,000+ AI models, datasets, papers from 13+ platforms. Hardware-aware model selection with VRAM and license constraints. 5 tools: search, rank, explain, select_model, compare.
Scored across 5 tools
The tools are mostly distinct (compare, explain, select_model) but search and rank overlap significantly; rank is described as 'mechanically the same keyword search' with task text folded in, which could confuse an agent about which to use. The descriptions attempt to disambiguate, but the boundary between keyword discovery and task-text ranking is blurry.
All tools follow a consistent pattern: 'free2aitools_' prefix with a lowercase snake_case verb or verb-noun combination (compare, explain, rank, search, select_model). Naming style is uniform and predictable.
Five tools is well-scoped for a read-only AI catalog discovery, filtering, comparison, and explanation server. Each tool has a clear role (with the minor overlap noted above), and none feel extraneous.
The tool surface covers the full lifecycle of catalog interaction: discovery (search), ranked discovery (rank), constraint-based filtering (select_model), side-by-side comparison (compare), and score explanation (explain). There are no obvious gaps for a read-only metadata catalog; all core operations are present.
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.
Inputs: ids (required).
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 = 0.35S + 0.25A + 0.15P + 0.15R + 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.
Inputs: id (required).
Keyword-search AI entities using the task/query text as input and return matching catalog entries. Search results are ordered by a relevance score based on the FNI and, where term-match data is available, how well the entry matches the query. The score used for ordering may differ from the fni_score field returned in the response. The result set is bounded. 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 that relevance score. 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.
Inputs: query (required), task, limit.
Keyword discovery over the Free2AITools catalog of AI models, datasets, papers, and tools. Returns matching catalog entries (metadata). Search results are ordered by a relevance score based on the FNI (Free2AITools Nexus Index) and, where term-match data is available, how well the entry matches the query. The score used for ordering may differ from the fni_score field returned in the response. The result set is bounded. The FNI is 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 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.
Inputs: query (required), type, limit.
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.
Inputs: task (required), limit, explain, constraints.
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