# FeatureJet

> FeatureJet — com-featurejet-feedback-board. Use this tool when you need to streamline AI agent development and feedback collection. FeatureJet solves the problem of inefficient communication between users and AI agents by providing a centralized feedback board with 22 integrated tools. It takes user votes as input and outputs actionable building blocks for AI agents over the MCP framework, ideal for use cases requiring collaborative and iterative AI model refinement.

Canonical page: https://skillsregistry.net/skills/com-featurejet-feedback-board  
JSON: https://api.skillsregistry.net/v1/skills/com-featurejet-feedback-board

## Description

The feedback board your AI agents work from: users vote, agents build over MCP. 22 tools.

## 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:** 2026-09-02

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/com.featurejet%2Ffeedback-board)

## Use it

MCP endpoint published by the skill: `https://mcp.featurejet.com/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": "com-featurejet-feedback-board"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/com-featurejet-feedback-board` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/com-featurejet-feedback-board/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