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mcp server

similarity-search-api-sdk

Stateless NMI + cosine fusion with entropy-driven alpha calibration

Description as published by the maintainer. Source

  • version 1.0.0
  • active

active — Registry entry last updated 2026-07-17.

What this server can do

3 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.

nexus_similarity_search_api_estimate_corpus_entropy_profile(n_bins, api_key, corpus_vectors)
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 a valid api_key (same as X-API-Key) and an x402 payment. Required: corpus_vectors, api_key.
nexus_similarity_search_api_rank_items_by_nmi_cosine_fusion(top_k, n_bins, api_key, query_vector, alpha_override, corpus_vectors)
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 a valid api_key (same as X-API-Key) and an x402 payment. Required: query_vector, corpus_vectors, api_key.
nexus_similarity_search_api_score_pair_nmi_cosine(alpha, n_bins, api_key, vector_a, vector_b)
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 a valid api_key (same as X-API-Key) and an x402 payment. Required: vector_a, vector_b, api_key.

Last successful function declaration observed on . Source: https://similarity-search-api-production.up.railway.app/mcp. We list what the server declared; we do not call any of these functions.

Endpoint status observed on . Source: https://similarity-search-api-production.up.railway.app/mcp.

Signals

These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.

Signal Value What it measures Window Observed Source
Latest published version 1.0.0 Latest version string the maintainer published to the registry. as of fetch Model Context Protocol
Registry record last updated 2026-07-17 When the registry record was last updated by its maintainer. point in time Model Context Protocol
First listed in the MCP Registry 2026-07-17 Date this server was first published to the official MCP Registry. Not a usage or quality measure. point in time Model Context Protocol
mcp tools declared 3 tools Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. as of probe similarity-search-api-production.up.railway.app
mcp endpoint status ok The server listed 3 functions when asked. as of probe similarity-search-api-production.up.railway.app

Where to get it

Also from nexus-mcp-infra

This record as data

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GET /api/v1/entries/mcp_server.json

Sources

  1. Official MCP Registry — Model Context Protocol, observed , trust tier 1.
  2. Tools declared by the MCP server at https://similarity-search-api-production.up.railway.app/mcp — similarity-search-api-production.up.railway.app, observed , trust tier 1.