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Supports using OpenAI models as independent raters for multi-model evaluation and statistical consensus measurement alongside other AI providers.

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conKurrence

by AlligatorC0der

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Remote

ConKurrence

One command. Find out if your AI agrees with itself.

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.

Install

npm install -g conkurrence

Related MCP server: CI-1T Prediction Stability Engine

MCP Server

Use ConKurrence as an MCP server in Claude Desktop or any MCP-compatible client:

npx conkurrence mcp

Claude Desktop Configuration

Add to your claude_desktop_config.json:

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

Claude Code Plugin

/plugin marketplace add AlligatorC0der/conkurrence

Features

Multi-model evaluation — Run your schema against Bedrock, OpenAI, and Gemini models simultaneously
Statistical rigor — Fleiss' kappa with bootstrap confidence intervals, Kendall's W for validity
Self-consistency mode — No API keys needed; uses the host model via MCP Sampling
Schema suggestion — AI-powered schema design from your data
Trend tracking — Compare runs over time, detect agreement degradation
Cost estimation — Know the cost before running

MCP Tools

Tool	Description
`conkurrence_run`	Execute an evaluation across multiple AI raters
`conkurrence_report`	Generate a detailed markdown report
`conkurrence_compare`	Side-by-side comparison of two runs
`conkurrence_trend`	Track agreement over multiple runs
`conkurrence_suggest`	AI-powered schema suggestion from your data
`conkurrence_validate_schema`	Validate a schema before running
`conkurrence_estimate`	Estimate cost and token usage

License

BUSL-1.1 — Business Source License 1.1

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

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