How does the scoring work?

The engine uses the BM25 algorithm, calculating IDF based on corpus size and term prevalence, then applying length normalization and saturation constants.

Do I need an LLM to calculate scores?

No, this tool provides deterministic mathematical results using the `compute_relevance_score` function without any LLM involvement.

What inputs are required for scoring?

You must provide query tokens, document tokens, corpus size, term document counts, and average document length.

BM25 Context Relevance Scorer MCP Connector for Claude

A+

Deterministic BM25 relevance scoring engine for RAG optimization.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner

More Details Connect to Claude

This MCP server provides a precision engine for calculating document relevance using the BM25 algorithm. It allows AI agents to solve the RAG relevance problem without relying on expensive LLM calls by providing deterministic, mathematical scores. Use compute_relevance_score to find how relevant a specific document is to a query, calculate_term_weights to compute term importance (IDF), and analyze_document_composition to extract structural statistics like token counts and frequencies.

bm25ragrelevancenlpinformation-retrieval

Connect to Claude

Subscribe on Vinkius, then add this connector to Claude.ai or Claude Code.

① Claude.ai (web app)

Go to Settings → Connectors → Add custom connector
Paste the MCP endpoint URL below

https://edge.vinkius.com/vk_preview_13iaLV42ZXoIPJuXWoZJmsXXcpfHRxpcEFkqQcfP/mcp

② Claude Code (terminal)

claude mcp add --transport http bm25-context-relevance-scorer https://edge.vinkius.com/vk_preview_13iaLV42ZXoIPJuXWoZJmsXXcpfHRxpcEFkqQcfP/mcp

③ Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "bm25-context-relevance-scorer": {
      "url": "https://edge.vinkius.com/vk_preview_13iaLV42ZXoIPJuXWoZJmsXXcpfHRxpcEFkqQcfP/mcp"
    }
  }
}

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