io.github.nexus-mcp-infra/similarity-search-api-sdk

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Au dernier audit MCP Hub, le serveur MCP « io.github.nexus-mcp-infra/similarity-search-api-sdk » est classé « Non encore testé ». Référencé dans l'annuaire. Seul le pentest comportemental fait foi, et il n'a pas encore été exécuté sur ce serveur. Le verdict est réévalué en continu ; consultez la fiche pour l'état à jour.

Méthode : MCP Hub analyse la description de chaque outil (couche 1) et, pour le verdict fort, exécute le serveur dans une micro-VM isolée avec leurres et réseau bloqué (couche 2). On publie des noms et des compteurs, jamais le contenu brut renvoyé par un outil.

Les 3 outils

• nexus_similarity_search_api_estimate_corpus_entropy_profile≈ 323 tokens

  Computes the aggregate entropy-calibrated alpha for a corpus without running a full search -- useful to inspect before committing to a large rank_items_by_nmi_cosine_fusion call. Returns a single aggregate corpus_entropy value, NOT a per-dimension breakdown -- the real logic only exposes the mean marginal entropy across dimensions, not H(X_d) per individual dimension. Do NOT use expecting per-dimension granularity. Requires an x402 payment.

  Paramètres, retour & exemple (2)

  Paramètres

  • n_bins · number · optionnel
    Number of histogram bins for entropy discretization. Must be between 3 and 50; should match the n_bins used in rank_items_by_nmi_cosine_fusion for the profile to be consistent.
  • corpus_vectors · array · requis
    List of dense numeric vectors for which to compute the aggregate entropy and calibrated alpha. Each inner array must be the same length. Maximum 500000 entries.

  Exemple d'appel

  {"name": "nexus_similarity_search_api_estimate_corpus_entropy_profile", "arguments": {"corpus_vectors": []}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• nexus_similarity_search_api_rank_items_by_nmi_cosine_fusion≈ 551 tokens

  Ranks a corpus of items against a query vector using a calibrated fusion score (alpha * cosine + (1-alpha) * NMI_normalizado), where alpha is auto-derived from the corpus's marginal entropy unless overridden. Results are identified by their 0-indexed position in corpus_vectors (this tool does not accept explicit item IDs). Use this when you need semantically-calibrated similarity over a stateless corpus of up to 500k items without a vector database. Do NOT use for purely geometric nearest-neighbor search where NMI overhead is unnecessary, nor for corpora larger than 500k items per call. Requires an x402 payment.

  Paramètres, retour & exemple (5)

  Paramètres

  • top_k · number · optionnel
    Number of top-ranked results to return, ordered by descending fusion score. Capped at 1000 by the core service regardless of corpus size.
  • n_bins · number · optionnel
    Number of histogram bins used to discretize continuous dimensions when estimating NMI. Must be between 3 and 50.
  • query_vector · array · requis
    Dense numeric vector representing the query item. Must have the same dimensionality as all corpus_vectors entries.
  • alpha_override · number · optionnel
    Fixed alpha weight for cosine component in [0.0, 1.0]. If omitted, alpha is auto-calibrated from corpus entropy. Set to 1.0 to use pure cosine; 0.0 for pure NMI.
  • corpus_vectors · array · requis
    List of dense numeric vectors forming the corpus to rank against. Each inner array must match query_vector dimensionality. Maximum 500000 entries.

  Exemple d'appel

  {"name": "nexus_similarity_search_api_rank_items_by_nmi_cosine_fusion", "arguments": {"query_vector": [], "corpus_vectors": []}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• nexus_similarity_search_api_score_pair_nmi_cosine≈ 399 tokens

  Computes the NMI-cosine fusion score for exactly one (query, target) vector pair at a fixed alpha. Use for explainability, debugging, or unit-level validation of fusion scores before running full corpus ranking. Unlike corpus-level ranking, alpha is NOT auto-calibrated for a single pair -- the real logic requires a fixed alpha (default 0.5); pass alpha explicitly for a specific blend. Do NOT use in a loop to score many pairs; batch them into rank_items_by_nmi_cosine_fusion instead. Requires an x402 payment.

  Paramètres, retour & exemple (4)

  Paramètres

  • alpha · number · optionnel
    Fixed alpha weight for the cosine component in [0.0, 1.0], applied as-is -- not auto-calibrated. Default 0.5 matches the core service default.
  • n_bins · number · optionnel
    Histogram bins for NMI discretization. Must be between 3 and 50.
  • vector_a · array · requis
    First dense numeric vector of the pair. Must have the same dimensionality as vector_b.
  • vector_b · array · requis
    Second dense numeric vector of the pair. Must have the same dimensionality as vector_a.

  Exemple d'appel

  {"name": "nexus_similarity_search_api_score_pair_nmi_cosine", "arguments": {"vector_a": [], "vector_b": []}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

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Source

https://similarity-search-api-production.up.railway.app/mcp