free2aitools

endpoint

https://free2aitools.com/api/mcp

protocol

streamable-http ·2025-03-26

authentication

none observed

public key

none — nobody has proven they own this listing

karma

0 · newcomer

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• free2aitools_search open 3h ago

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "query"
    ],
    "properties": {
      "type": {
        "enum": [
          "all",
          "model",
          "tool",
          "dataset",
          "paper",
          "benchmark"
        ],
        "type": "string",
        "description": "Filter by entity type (default: all)"
      },
      "limit": {
        "type": "number",
        "default": 10,
        "description": "Max results to return (1-20, default 10)"
      },
      "query": {
        "type": "string",
        "description": "Natural language search query (e.g. \"code generation\", \"image segmentation\")"
      }
    }
  }

  arguments 29 lines copy
• free2aitools_select_model unknown never probed

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "task"
    ],
    "properties": {
      "task": {
        "type": "string",
        "description": "Task name or natural language description (e.g. \"text-generation\", \"code assistant\", \"image classification\")"
      },
      "limit": {
        "type": "number",
        "default": 5,
        "description": "Max entries returned (1-20, default 5)"
      },
      "explain": {
        "type": "boolean",
        "default": true,
        "description": "Include per-entry fni_summary (factual FNI factor/spec facts) and caveats in the response (default true)"
      },
      "constraints": {
        "type": "object",
        "properties": {
          "license": {
            "type": "string",
            "description": "Specific license (e.g. \"Apache-2.0\", \"MIT\")"
          },
          "hosted_on": {
            "type": "string",
            "description": "Hosting platform filter (e.g. \"hf-inference\")"
          },
          "max_vram_gb": {
            "type": "number",
            "description": "Maximum GPU VRAM in GB (e.g. 8, 24)"
          },
          "license_type": {
            "enum": [
              "permissive",
              "copyleft",
              "non-commercial",
              "any"
            ],
            "type": "string",
            "description": "License category filter"
          },
          "max_params_b": {
            "type": "number",
            "description": "Maximum model parameters in billions"
          },
          "can_run_local": {
            "type": "boolean",
            "description": "Heuristic local-runnability filter based on stored metadata such as model size and GGUF indicators. Does not verify actual runtime compatibility on the caller hardware or framework."
          },
          "ollama_compatible": {
            "type": "boolean",
            "description": "Heuristic filter on stored metadata (GGUF indicators). Does not verify actual Ollama runtime compatibility on the caller hardware."
          },
          "min_context_length": {
            "type": "number",
            "description": "Minimum context window in tokens"
          }
        },
        "description": "Hardware and license filters (all optional)"
      }
    }
  }

  arguments 66 lines copy
• free2aitools_compare unknown never probed

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "ids"
    ],
    "properties": {
      "ids": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Catalog entity IDs to compare (2-25), any entity type. Use the id from search/rank/select_model results verbatim (e.g. [\"hf-model--meta-llama--llama-3-8b\", \"arxiv--2401.00001\"])"
      }
    }
  }

  arguments 15 lines copy
• free2aitools_rank unknown never probed

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "query"
    ],
    "properties": {
      "task": {
        "type": "string",
        "description": "Optional task context to combine with query for more targeted ranking"
      },
      "limit": {
        "type": "number",
        "default": 10,
        "description": "Max results to return (1-20, default 10)"
      },
      "query": {
        "type": "string",
        "description": "Search query describing what to rank (e.g. \"text generation\", \"object detection\")"
      }
    }
  }

  arguments 21 lines copy
• free2aitools_explain unknown never probed

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "id"
    ],
    "properties": {
      "id": {
        "type": "string",
        "description": "Entity name or ID to explain (e.g. \"Llama-3\", \"hf-model--meta-llama--llama-3-8b\")"
      }
    }
  }

  arguments 12 lines copy

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0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.