# User Intent MCP

> Use this tool when you need to collect user input in multiple formats, such as text and images, to understand user intent and inform AI decision-making. It solves problems of limited user input modalities and enables AI agents to engage in more natural and interactive conversations. The tool accepts natural language questions as input and returns user responses in text and image formats.

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

## Description

A multi-modal user intent collection MCP server that allows AI agents to ask users questions and receive text/image replies.

## Trust

- **Trust score (0–1):** 0.23
- **Verification tier:** scanned
- **Last scanned:** 2026-08-31

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** media
- **Updated:** 2026-08-31

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/hy5bjm1h6a)
- **Repository:** <https://github.com/chameleonscool/feedback-mcp>

## 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": "chameleonscool-feedback-mcp"
    }
  }
}
```

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