Similarity Search API SDK MCP Server

by nexus-mcp-infra Your server? Claim it

answering

Similarity Search API SDK is answering right now. Last checked 11 min ago. It exposes 3 tools. Last commit 1 Sep 2026.

Stateless NMI + cosine fusion with entropy-driven alpha calibration

Uptime history 47 days of history · worst day 97%

47 days agonow

100.0%

Uptime 24h

91 of 91 checks

Tools

read from the server

908 ms

Response time

average over 24h

Stars

last commit 1 Sep 2026

What changed 6

Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 25 August 2026. No other catalogue keeps this.

25 Aug 3 tool descriptions were rewritten nexus_similarity_search_api_estimate_corpus_entropy_profile, nexus_similarity_search_api_rank_items_by_nmi_cosine_fusion, nexus_similarity_search_api_score_pair_nmi_cosine

25 Aug 3 tools changed the parameters they ask for nexus_similarity_search_api_estimate_corpus_entropy_profile, nexus_similarity_search_api_rank_items_by_nmi_cosine_fusion, nexus_similarity_search_api_score_pair_nmi_cosine

What the code does

We read the source, 21 h ago · tools taken from the live server · rules 3dff92dd89df

Capabilities

What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.

Runs an external command patch_sdk_x402_only_auth_regrounding.py:212

    result = subprocess.run(["node", "--check", JS_PATH], capture_output=True, text=True)

Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.

This code can reach further than it looks

We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 11 min ago.

Claude Code Claude Desktop Codex Cursor VS Code

run in your terminal

claude mcp add similarity-search-api-sdk --transport http https://similarity-search-api-production.up.railway.app/mcp

~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "similarity-search-api-sdk": {
      "url": "https://similarity-search-api-production.up.railway.app/mcp"
    }
  }
}

~/.codex/config.toml

[mcp_servers.similarity-search-api-sdk]
url = "https://similarity-search-api-production.up.railway.app/mcp"

.cursor/mcp.json

{
  "mcpServers": {
    "similarity-search-api-sdk": {
      "url": "https://similarity-search-api-production.up.railway.app/mcp"
    }
  }
}

.vscode/mcp.json

{
  "mcpServers": {
    "similarity-search-api-sdk": {
      "url": "https://similarity-search-api-production.up.railway.app/mcp"
    }
  }
}

Available tools 3

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

nexus

nexus_similarity_search_api_estimate_corpus_entropy_profile

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.

nexus_similarity_search_api_rank_items_by_nmi_cosine_fusion

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.

nexus_similarity_search_api_score_pair_nmi_cosine

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.

Endpoints

URL	Transport	State	Latency	Checked
https://similarity-search-api-production.up.railway.app/mcp	streamable-http	answering	1069 ms	11 min ago

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Similarity Search API SDK — questions

Answers built from our own checks of this server.

What can Similarity Search API SDK do?

It exposes 3 tools, read directly from the server on our last check. Among them: nexus_similarity_search_api_estimate_corpus_entropy_profile, nexus_similarity_search_api_rank_items_by_nmi_cosine_fusion, nexus_similarity_search_api_score_pair_nmi_cosine. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →

Is Similarity Search API SDK working right now?

We send a real MCP handshake every 15 minutes. Over the last 24 hours 91 of 91 checks got a reply (100.0%), average response time 908 ms. The bar chart above shows every period we have measured.

How do I connect Similarity Search API SDK?

Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.

Does Similarity Search API SDK need an API key?

No. Similarity Search API SDK completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 3 of them are readable on this page. This is what we observed, not what the docs claim.

How fast is Similarity Search API SDK?

It answers our handshake in 908 ms on average, which is faster than 11% of all working MCP servers we measure. That is on the slow side — worth knowing if the tool sits inside an interactive loop. The comparison comes from our own checks across the whole registry, every 15 minutes.

Is Similarity Search API SDK open source?

We cannot say either way: written in Python and 0 stars on GitHub, but we could not determine the licence, and without one the code is not open source by default.