{"serverId":"mcp-registry/io.github.nexus-mcp-infra/similarity-search-api-sdk","tools":[{"name":"nexus_similarity_search_api_estimate_corpus_entropy_profile","fixed":{"name":"nexus_similarity_search_api_estimate_corpus_entropy_profile","description":"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.","inputSchema":{"type":"object","title":"estimate_corpus_entropy_profileArguments","required":["corpus_vectors"],"properties":{"n_bins":{"type":"number","title":"N Bins","default":16,"maximum":50,"minimum":3,"description":"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":{"type":"array","items":{"type":"array","items":{"type":"number"}},"title":"Corpus Vectors","maxItems":500000,"minItems":1,"description":"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."}}},"annotations":{"readOnlyHint":true}},"changes":[{"kind":"annotations","detail":"Added readOnlyHint: true (inferred from the name).","verify":true}],"tokensBefore":348,"tokensAfter":348},{"name":"nexus_similarity_search_api_rank_items_by_nmi_cosine_fusion","fixed":{"name":"nexus_similarity_search_api_rank_items_by_nmi_cosine_fusion","description":"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-neighb","inputSchema":{"type":"object","title":"rank_items_by_nmi_cosine_fusionArguments","required":["query_vector","corpus_vectors"],"properties":{"top_k":{"type":"number","title":"Top K","default":10,"maximum":1000,"minimum":1,"description":"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":{"type":"number","title":"N Bins","default":16,"maximum":50,"minimum":3,"description":"Number of histogram bins used to discretize continuous dimensions when estimating NMI. Must be between 3 and 50."},"query_vector":{"type":"array","items":{"type":"number"},"title":"Query Vector","maxItems":4096,"minItems":2,"description":"Dense numeric vector representing the query item. Must have the same dimensionality as all corpus_vectors entries."},"alpha_override":{"type":"number","title":"Alpha Override","default":null,"maximum":1,"minimum":0,"description":"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":{"type":"array","items":{"type":"array","items":{"type":"number"}},"title":"Corpus Vectors","maxItems":500000,"minItems":1,"description":"List of dense numeric vectors forming the corpus to rank against. Each inner array must match query_vector dimensionality. Maximum 500000 entries."}}},"annotations":{"readOnlyHint":true}},"changes":[{"kind":"annotations","detail":"Added readOnlyHint: true (inferred from the name).","verify":true}],"tokensBefore":556,"tokensAfter":556},{"name":"nexus_similarity_search_api_score_pair_nmi_cosine","fixed":{"name":"nexus_similarity_search_api_score_pair_nmi_cosine","description":"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 x","inputSchema":{"type":"object","title":"score_pair_nmi_cosineArguments","required":["vector_a","vector_b"],"properties":{"alpha":{"type":"number","title":"Alpha","default":0.5,"maximum":1,"minimum":0,"description":"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":{"type":"number","title":"N Bins","default":16,"maximum":50,"minimum":3,"description":"Histogram bins for NMI discretization. Must be between 3 and 50."},"vector_a":{"type":"array","items":{"type":"number"},"title":"Vector A","maxItems":4096,"minItems":2,"description":"First dense numeric vector of the pair. Must have the same dimensionality as vector_b."},"vector_b":{"type":"array","items":{"type":"number"},"title":"Vector B","maxItems":4096,"minItems":2,"description":"Second dense numeric vector of the pair. Must have the same dimensionality as vector_a."}}},"annotations":{"readOnlyHint":true}},"changes":[{"kind":"annotations","detail":"Added readOnlyHint: true (inferred from the name).","verify":true}],"tokensBefore":421,"tokensAfter":421}],"toolsChanged":3,"tokensSaved":0,"todo":[],"note":"Deterministic fixes. Changes marked verify need a human look before shipping (renames, inferred annotations, inferred types, removed text). Parameter descriptions in `todo` need to be written by someone who knows the API."}