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Free2AITools — AI Model Discovery API & MCP Server

@mosesy5688-cell

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基本情報

カテゴリ

開発者ツール

ライセンス

MIT

トランスポート

stdio

作者

mosesy5688-cell

投稿者

mosesy5688-cell

Free2AITools — AI Model Discovery API & MCP Server について

概要はまだありません

#ai-models#model-discovery#model-selection#huggingface#ranking#search

Free2AITools — AI Model Discovery API & MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "free2aitools": {
      "url": "https://free2aitools.com/api/mcp",
      "transport": "streamable-http"
    }
  }
}

Free2AITools — AI Model Discovery API & MCP Server のツール

5

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.

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.

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.

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.

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.

概要

What is Free2AITools — AI Model Discovery API & MCP Server?

A free, open-source registry and API for discovering, ranking, and comparing AI models, datasets, papers, tools, and benchmarks. It aggregates data daily from HuggingFace, GitHub, ArXiv, Ollama, Replicate, Civitai, and more, and scores entries using the Free2AITools Nexus Index (FNI) — a five-factor composite score. Designed for developers and AI agents.

How to use Free2AITools — AI Model Discovery API & MCP Server?

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.

Key features of Free2AITools — AI Model Discovery API & MCP Server

  • 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

Use cases of Free2AITools — AI Model Discovery API & MCP Server

  • Select the best text‑generation model fitting within 8GB VRAM
  • Compare two models side‑by‑side using FNI factors
  • Search for code‑generation tools and rank results
  • Embed live FNI scores as badges in project READMEs
  • Offline analysis of the full catalog with DuckDB or Pandas

よくある質問

1

What data sources does Free2AITools use?

It aggregates models, datasets, papers, tools, and benchmarks from HuggingFace, GitHub, ArXiv, Ollama, Replicate, Civitai, and more.

2

How is the FNI score calculated?

The FNI (Free2AITools Nexus Index) is a five‑factor composite score covering Semantic, Authority, Popularity, Recency, and Quality. The full breakdown is returned in every API response.

3

Does it require authentication or API keys?

No. The API and MCP server are free to use without any authentication.

4

What are the runtime requirements for the MCP server?

The MCP server is a remote endpoint (URL: `https://free2aitools.com/api/mcp`) — no local installation needed. Just configure the URL in your MCP‑compatible client.

5

What are the available MCP tools?

Five tools: `free2aitools_search`, `free2aitools_rank`, `free2aitools_explain`, `free2aitools_select_model`, and `free2aitools_compare`.

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