# Feedback1

> Feedback1 — ai-feedback1-mcp. Use this tool when you need to collect and manage feedback from AI agents to inform product development and roadmap decisions. It solves problems related to prioritizing features and understanding user needs, allowing for data-driven decision making. The tool accepts feedback inputs and outputs actionable insights to guide product strategy.

Canonical page: https://skillsregistry.net/skills/ai-feedback1-mcp  
JSON: https://api.skillsregistry.net/v1/skills/ai-feedback1-mcp

## Description

Collect feedback and manage the product roadmap from AI agents.

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-28

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/ai.feedback1%2Fmcp)

## Use it

MCP endpoint published by the skill: `https://app.feedback1.ai/api/mcp`

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": "ai-feedback1-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ai-feedback1-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ai-feedback1-mcp/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