Agent Reputation MCP Server

Agent Reputation MCP Server

Enables AI agents to rate each other, track interaction outcomes, and compute trust scores, with features like submitting ratings, getting reputation profiles, leaderboards, and trust computation.

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Agent Reputation MCP Server

MCP server for AI agent trust scores, ratings, and reputation tracking. Enables AI agents to rate each other, track interaction outcomes, and compute trust scores.

Features

  • submit_rating — Submit a trust rating (1-5) for an AI agent with category and comment
  • get_reputation — Full reputation profile with score distribution and category breakdown
  • get_leaderboard — Top-rated agents overall or filtered by category
  • report_interaction — Log interaction outcomes (success/failure) for trust computation
  • get_trust_score — Computed trust score (0-100) based on ratings + interactions + volume
  • compare_agents — Side-by-side reputation comparison of multiple agents

Installation

npm install -g @aiagentkarl/agent-reputation-mcp-server

Usage with Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "agent-reputation": {
      "command": "npx",
      "args": ["-y", "@aiagentkarl/agent-reputation-mcp-server"]
    }
  }
}

Data Storage

All data is stored locally in ~/.agent-reputation/ as JSON files:

  • ratings.json — All submitted ratings
  • interactions.json — All logged interactions

Trust Score Computation

The trust score (0-100) is computed from three components:

Component Max Points Source
Rating Score 50 Average rating * 10
Interaction Score 30 Success rate * 30
Volume Bonus 20 log10(total_count + 1) * 10

Trust Levels

Score Level
80-100 Highly Trusted
60-79 Trusted
40-59 Neutral
20-39 Low Trust
0-19 Untrusted

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License

MIT

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