Agent Feedback Loop MCP Server

1

Add it to Claude Code

Run this in a terminal.

Run in terminal
claude mcp add agent-feedback -- uvx agent-feedback-mcp-server
README.md

Community-driven quality signals for MCP tools.

Agent Feedback Loop 📊

Community-driven quality signals for MCP tools. Agents report tool results, building a quality database that helps all agents pick better tools.

The Problem

Agents don't know which MCP tools are reliable. They try tools blindly and hope for the best.

The Solution

Automated feedback: agents report success/failure and quality after each tool call. Over time, a quality database emerges that helps every agent make better decisions.

Installation

pip install agent-feedback-mcp-server
{
  "mcpServers": {
    "feedback": {
      "command": "uvx",
      "args": ["agent-feedback-mcp-server"]
    }
  }
}

Tools

Tool Description
report_tool_result Report success/failure and quality score
get_tool_quality Get quality metrics for a specific tool
get_best_tools Find highest-rated tools (optionally by task)
get_trending_tools See what's trending recently

Network Effect

More agents reporting → Better quality data → Better tool choices → More agents using → More reports. The database gets better with every user.


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License

MIT

Tools (4)

report_tool_resultReport success/failure and quality score for a tool execution.
get_tool_qualityGet quality metrics for a specific tool.
get_best_toolsFind highest-rated tools, optionally filtered by task.
get_trending_toolsSee what tools are trending recently.

Configuration

claude_desktop_config.json
{"mcpServers": {"feedback": {"command": "uvx", "args": ["agent-feedback-mcp-server"]}}}

Try it

Report that the file-search tool failed to find the requested document.
What is the current quality rating for the web-scraper tool?
Find the best-rated tools for performing data analysis tasks.
Show me the trending MCP tools based on recent agent feedback.

Frequently Asked Questions

What are the key features of Agent Feedback Loop?

Automated reporting of tool success and failure rates. Community-driven quality database for MCP tools. Querying tool performance metrics and quality scores. Discovery of trending and top-rated tools for specific tasks.

What can I use Agent Feedback Loop for?

Agents selecting the most reliable tool for a specific task based on historical success rates. Developers monitoring the performance and reliability of their published MCP tools. Building a community-verified registry of high-quality AI agent tools.

How do I install Agent Feedback Loop?

Install Agent Feedback Loop by running: pip install agent-feedback-mcp-server

What MCP clients work with Agent Feedback Loop?

Agent Feedback Loop works with any MCP-compatible client including Claude Desktop, Claude Code, Cursor, and other editors with MCP support.

Turn this server into reusable context

Keep Agent Feedback Loop docs, env vars, and workflow notes in Conare so your agent carries them across sessions.

Need the old visual installer? Open Conare IDE.
Open Conare