# mcp-tool-feedback

> mcp-tool-feedback — duncanapm-mcp-tool-feedback. Use this tool when you need to collect and organize structured feedback from AI agents interacting with MCP servers, helping to identify capability gaps and improve system performance. It solves the problem of unstructured and disorganized feedback by providing a standardized reporting mechanism. The tool takes in agent reports and outputs actionable insights, ideal for use cases where continuous improvement and iteration are crucial.

Canonical page: https://skillsregistry.net/skills/duncanapm-mcp-tool-feedback  
JSON: https://api.skillsregistry.net/v1/skills/duncanapm-mcp-tool-feedback

## Description

Structured agent feedback for MCP servers. When an agent hits a capability gap, it files a structured report.

## Trust

- **Trust score (0–1):** 0.98
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** file-system
- **Updated:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/duncanapm/mcp-tool-feedback)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "duncanapm-mcp-tool-feedback"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/duncanapm-mcp-tool-feedback` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/duncanapm-mcp-tool-feedback/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt