mcpbeat

Similarity Search API SDK MCP Server

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

Similarity Search API SDK is answering right now. Last checked 9 min ago. It exposes 3 tools. Last commit 28 Jul 2026.

Stateless NMI + cosine fusion with entropy-driven alpha calibration

Uptime history 40 hours of history
40 hours agonow
100.0%
Uptime 24h
91 of 91 checks
3
Tools
read from the server
354 ms
Response time
average over 24h
0
Stars
last commit 28 Jul 2026

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 9 min ago.

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 a valid api_key (same as X-API-Key) and 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 a valid api_key (same as X-API-Key) and 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 a valid api_key (same as X-API-Key) and an x402 payment.

Endpoints

URLTransportStateLatencyChecked
https://similarity-search-api-production.up.railway.app/mcp streamable-http answering 332 ms 9 min ago

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 354 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 354 ms on average, which is faster than 39% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.
Is Similarity Search API SDK open source?
Yes — written in Python and 0 stars on GitHub. The source link is on this page, so you can read exactly what it does with your data before you connect it.