Agent Feedback Loop
# 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
```bash
pip install agent-feedback-mcp-server
```
```json
{
"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.
---
## More MCP Servers by AiAgentKarl
| Category | Servers |
|----------|---------|
| 🔗 Blockchain | [Solana](https://github.com/AiAgentKarl/solana-mcp-server) |
| 🌍 Data | [Weather](https://github.com/AiAgentKarl/weather-mcp-server) · [Germany](https://github.com/AiAgentKarl/germany-mcp-server) · [Agriculture](https://github.com/AiAgentKarl/agriculture-mcp-server) · [Space](https://github.com/AiAgentKarl/space-mcp-server) · [Aviation](https://github.com/AiAgentKarl/aviation-mcp-server) · [EU Companies](https://github.com/AiAgentKarl/eu-company-mcp-server) |
| 🔒 Security | [Cybersecurity](https://github.com/AiAgentKarl/cybersecurity-mcp-server) · [Policy Gateway](https://github.com/AiAgentKarl/agent-policy-gateway-mcp) · [Audit Trail](https://github.com/AiAgentKarl/agent-audit-trail-mcp) |
| 🤖 Agent Infra | [Memory](https://github.com/AiAgentKarl/agent-memory-mcp-server) · [Directory](https://github.com/AiAgentKarl/agent-directory-mcp-server) · [Hub](https://github.com/AiAgentKarl/mcp-appstore-server) · [Reputation](https://github.com/AiAgentKarl/agent-reputation-mcp-server) |
| 🔬 Research | [Academic](https://github.com/AiAgentKarl/crossref-academic-mcp-server) · [LLM Benchmark](https://github.com/AiAgentKarl/llm-benchmark-mcp-server) · [Legal](https://github.com/AiAgentKarl/legal-court-mcp-server) |
[→ Full catalog (40+ servers)](https://github.com/AiAgentKarl)
## License
MIT
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: get_best_tools retrieves top-rated tools, get_tool_quality provides metrics for a specific tool, get_trending_tools shows recent trends, and report_tool_result submits feedback. There is no overlap in functionality, making tool selection unambiguous.
All tool names follow a consistent verb_noun pattern with underscores (e.g., get_best_tools, get_tool_quality, get_trending_tools, report_tool_result). The naming is predictable and readable throughout the set.
With 4 tools, the count is reasonable for a feedback loop server, covering core operations like querying and reporting. It might feel slightly thin if advanced features like analytics or bulk operations are needed, but it's well-scoped for basic functionality.
The tool set covers key aspects of a feedback system: retrieving best tools, checking specific tool quality, viewing trends, and submitting reports. A minor gap is the lack of tools for managing or aggregating feedback data, but agents can work around this with the provided operations.