Conkurrence

Name: Conkurrence
Author: AlligatorC0der

@AlligatorC0der

Conkurrence について

Conkurrence measures whether multiple AI models produce consistent outputs on your evaluation tasks. It tells you which items your AI agrees on and which need human review — using Fleiss' κ, Kendall's W, and bootstrap confidence intervals, the same psychometric methods trusted in

#ai-evaluation #golden-dataset #llm-evaluation #testing #inter-rater-reliability #fleiss-kappa #kendall-w #validating-prompt-output #consensus #multi-model

基本情報

カテゴリ

その他

トランスポート

stdio

公開者

AlligatorC0der

投稿者

joe Etherage

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "conkurrence": {
      "command": "npx",
      "args": [
        "-y",
        "conkurrence",
        "mcp"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Conkurrence?

ConKurrence is a statistically validated consensus measurement toolkit for AI evaluation pipelines. It uses multiple AI models as independent raters, measures inter-rater reliability with Fleiss' kappa and bootstrap confidence intervals, and routes contested items to human experts.

How to use Conkurrence?

Install globally with npm install -g conkurrence, then run as an MCP server with npx conkurrence mcp. Add the corresponding configuration to your Claude Desktop or Claude Code plugin settings. Invoke the available MCP tools such as conkurrence_run, conkurrence_report, conkurrence_compare, conkurrence_trend, conkurrence_suggest, conkurrence_validate_schema, or conkurrence_estimate.