featuriq-mcp

An MCP (Model Context Protocol) server for Featuriq — the product feedback and roadmap tool for PMs.

Connect your Featuriq workspace to any MCP-compatible AI client (Claude Desktop, Cursor, etc.) and query your feature requests, search customer feedback, run AI prioritization, update statuses, and notify users — all from natural language.

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Installation

Option 1 — run directly with npx (no install required)

npx featuriq-mcp

Option 2 — install globally

npm install -g featuriq-mcp
featuriq-mcp

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Setup

1. Get your API key

Log in to featuriq.io, go to Settings → API, and copy your API key.

2. Set the environment variable

export FEATURIQ_API_KEY=fq_live_xxxxxxxxxxxxxxxxxxxx

Or copy .env.example to .env and fill in your key if your client supports .env files.

| Variable | Required | Default | Description |
|---|---|---|---|
| FEATURIQ_API_KEY | Yes | — | Your Featuriq API key |
| FEATURIQ_API_URL | No | https://featuriq.io/v1 | Override the API base URL |

3. Add to your MCP client

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "featuriq": {
      "command": "npx",
      "args": ["featuriq-mcp"],
      "env": {
        "FEATURIQ_API_KEY": "fq_live_xxxxxxxxxxxxxxxxxxxx"
      }
    }
  }
}

Cursor

Add to your Cursor MCP settings:

{
  "featuriq": {
    "command": "npx featuriq-mcp",
    "env": {
      "FEATURIQ_API_KEY": "fq_live_xxxxxxxxxxxxxxxxxxxx"
    }
  }
}

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Available Tools

get_top_requests

Returns the top feature requests sorted by vote count or revenue impact.

Parameters:
- limit (number, default 10) — how many results to return
- sort_by ("votes" | "revenue_impact", default "votes") — sort order

Example prompts:
- "What are the top 5 most-requested features?"
- "Show me the highest revenue impact requests."

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search_feedback

Semantically searches all feedback posts using natural language — finds relevant results even when the exact words don't match.

Parameters:
- query (string) — what to search for
- limit (number, default 10) — max results

Example prompts:
- "Find feedback about slow dashboard loading."
- "Search for requests related to CSV export."
- "What are users saying about mobile performance?"

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get_feature_feedback

Returns all comments and discussion for a specific feature request.

Parameters:
- feature_id (string) — the feature's unique ID

Example prompts:
- "Show me all feedback on feature feat_01j8k..."
- "What are users saying about the API rate limit request?"

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get_prioritization

Returns an AI-prioritized list of features, scored across the factors you choose.

Parameters:
- factors (array) — one or more of: "votes", "revenue", "effort", "strategic_fit"
- limit (number, default 10)

Example prompts:
- "Prioritize our backlog by votes and revenue impact."
- "Give me the top 10 features ranked by votes, effort, and strategic fit."
- "What should we build next quarter based on revenue and strategic alignment?"

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update_feature_status

Updates the status of a feature request.

Parameters:
- feature_id (string) — the feature's unique ID
- status ("planned" | "in_progress" | "shipped" | "closed")

Example prompts:
- "Mark feature feat_01j8k as in_progress."
- "Set the dark mode request to shipped."
- "Close the feature request for legacy IE support."

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notify_requesters

Sends a personalized notification to every user who voted for a feature.

Parameters:
- feature_id (string) — which feature's voters to notify
- message (string) — the message to send (Featuriq personalizes it per recipient)

Example prompts:
- "Notify everyone who requested CSV export that it's now live."
- "Tell the users who voted for dark mode that we're starting work on it next sprint."

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create_post

Creates a new feedback post on a Featuriq board.

Parameters:
- board_id (string) — which board to post to
- title (string) — short title for the post
- description (string) — full description

Example prompts:
- "Log a feature request for bulk CSV import on the features board."
- "Create a post for the Slack integration idea from today's customer call."

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Available Resources

Resources are data sources that the AI can read at any time for context.

featuriq://roadmap

The current roadmap grouped by status: In Progress, Planned, and Recently Shipped.

Example prompts:
- "What's on our current roadmap?"
- "What features are in progress right now?"

featuriq://changelog

The last 20 shipped features with ship dates and release notes.

Example prompts:
- "What have we shipped recently?"
- "Write a summary of our last month's product updates."

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Example Conversation

> You: What are the top feature requests we haven't started yet, and which ones should we prioritize based on votes and revenue impact?
>
> Claude: (calls get_top_requests and get_prioritization) Here are your top unstarted requests...

> You: Great. Mark the #1 one as in_progress and notify everyone who voted for it.
>
> Claude: (calls update_feature_status then notify_requesters) Done! Status updated and 47 users notified.

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Development

git clone https://github.com/carlosalvite/featuriq-mcp
cd featuriq-mcp
npm install
npm run build
FEATURIQ_API_KEY=your_key node dist/index.js

To watch for changes during development:

npm run dev

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License

MIT © Featuriq