FeaturePulse MCP Server

A Model Context Protocol (MCP) server for FeaturePulse feedback management. Connect FeaturePulse to any MCP-compatible AI client to query feature requests, analyze MRR impact, and manage your product roadmap through natural language.

Features

• 5 Tools — Feature requests, stats, search, grouping, and status updates
• MRR Data — Every request includes revenue impact from paying customers
• Search & Filter — By status, priority, votes, or free-text search
• Write Access — Update feature request status and priority directly

Prerequisites

• Node.js v18+
• MCP Client — Claude Code, Claude Desktop, Cursor, Windsurf, or any MCP-compatible client
• FeaturePulse API Key — Get one from your FeaturePulse dashboard under Project Settings

Quick Start with Claude Code

The fastest way to start — run npx directly through Claude Code. No clone, no build.

Step 1: Get Your API Key

1. Go to your FeaturePulse dashboard
2. Open Project Settings
3. Copy your API Key

Step 2: Add the MCP Server

claude mcp add --transport stdio featurepulse \
  --scope user \
  --env FEATUREPULSE_API_KEY=<YOUR_API_KEY> \
  -- npx -y featurepulse-mcp

Replace <YOUR_API_KEY> with your API key.

Step 3: Restart Claude Code

Quit and reopen Claude Code for the new server to load.

Step 4: Verify

Ask Claude:

List the available FeaturePulse tools.

You should see 5 tools including list_feature_requests and get_project_stats.

Setup with Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "featurepulse": {
      "command": "npx",
      "args": ["-y", "featurepulse-mcp"],
      "env": {
        "FEATUREPULSE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Setup with Cursor / Windsurf

Add the same configuration to your editor's MCP settings file. Both Cursor and Windsurf support the MCP standard.

Available Tools

| Tool | Type | Description | |------|------|-------------| | list_feature_requests | Read | Browse and filter feature requests with MRR data. Filter by status, priority; sort by votes, MRR, or date. | | get_project_stats | Read | High-level overview — total requests, votes, MRR by status and priority. Top 10 by votes and MRR. | | search_feedback | Read | Full-text search across feature request titles. | | analyze_feedback_by_group | Read | Group requests by status or priority with aggregated counts and MRR. | | update_feature_status | Write | Change the status, priority, or status message of a feature request. |

Example Prompts

• "What are the top feature requests by MRR?"
• "Show me all pending high-priority requests"
• "How much revenue is behind planned features?"
• "Search for feedback about dark mode"
• "Mark the dark mode request as in_progress"
• "Give me a summary of feature requests grouped by status"

Configuration

| Variable | Required | Description | |----------|----------|-------------| | FEATUREPULSE_API_KEY | Yes | Your project API key from the FeaturePulse dashboard | | FEATUREPULSE_URL | No | API base URL (defaults to https://featurepul.se) |

How It Works

AI Assistant  ←→  MCP Server (stdio/JSON-RPC)  ←→  FeaturePulse API (HTTPS)

The MCP server communicates over stdio using JSON-RPC. When your AI assistant calls a tool (e.g. list_feature_requests), the server makes authenticated requests to the FeaturePulse API and returns formatted results.

Development

cd mcp-server
npm install
npm run dev    # Run with tsx (auto-reload)
npm run build  # Compile TypeScript
npm start      # Run compiled version

Testing with MCP Inspector

npx @modelcontextprotocol/inspector npx featurepulse-mcp

License

MIT