Featuriq MCP Server

MCP server for Featuriq — query and manage product feedback and roadmaps from any AI client

Local serverstdioGo

What is the Featuriq MCP MCP server?

Featuriq MCP MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. MCP server for Featuriq — query and manage product feedback and roadmaps from any AI client.

What you get

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.

What the assistant can call

Once Featuriq MCP is connected, these are the calls the assistant has available:

  • Cursor — The Cursor tool exposed by this server
  • get_top_requests — Returns the top feature requests sorted by vote count or revenue impact
  • search_feedback — Semantically searches all feedback posts using natural language — finds relevant results even when the exact words don't match
  • get_feature_feedback — Returns all comments and discussion for a specific feature request
  • get_prioritization — Returns an AI-prioritized list of features, scored across the factors you choose
  • update_feature_status — The update_feature_status tool exposed by this server
  • notify_requesters — Sends a personalized notification to every user who voted for a feature
  • create_post — The create_post tool exposed by this server

Setting it up

The server ships on npm as featuriq-mcp, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.

Configuration and credentials

You will need 2 environment variables: FEATURIQ_API_KEY, FEATURIQ_API_URL. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.

Choosing this one

Plenty of developer tooling servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Featuriq MCP's toolset — Cursor, get_top_requests, search_feedback and 5 more — is a fair guide to whether it matches your workflow. It is maintained by carlosalvite; worth a glance at recent repository activity before you build anything load-bearing on it.

We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.

Before you rely on it

  • It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
  • Missing credentials fail quietly in some clients — if no tools show up, check the environment block first.
  • MCP clients confirm each tool call by default. Leave that on until you have watched what the featuriq mcp mcp server does with a few real requests.

Available tools

ToolWhat it does
CursorThe Cursor tool exposed by this server.
get_top_requestsReturns the top feature requests sorted by vote count or revenue impact.
search_feedbackSemantically searches all feedback posts using natural language — finds relevant results even when the exact words don't match.
get_feature_feedbackReturns all comments and discussion for a specific feature request.
get_prioritizationReturns an AI-prioritized list of features, scored across the factors you choose.
update_feature_statusThe update_feature_status tool exposed by this server.
notify_requestersSends a personalized notification to every user who voted for a feature.
create_postThe create_post tool exposed by this server.

How to install the Featuriq MCP MCP server

{
  "mcpServers": {
    "featuriq": {
      "command": "npx",
      "args": ["-y", "featuriq-mcp"],
      "env": {
        "FEATURIQ_API_KEY": "your-value",
        "FEATURIQ_API_URL": "your-value"
      }
    }
  }
}

Add to claude_desktop_config.json, then restart Claude Desktop.

Configuration

VariableDescriptionRequired
FEATURIQ_API_KEYCredential the server authenticates with.Yes
FEATURIQ_API_URLEndpoint or connection string the server talks to.Yes

Example prompts to try

  • Use Featuriq MCP to Cursor.
  • Use Featuriq MCP to get top requests.
  • Use Featuriq MCP to search feedback.

Frequently asked questions

It connects Featuriq MCP to MCP-compatible AI assistants such as Claude and Cursor, exposing 8 tools (Cursor, get_top_requests, search_feedback, and more) that the assistant can call on your behalf. Instead of copying data back and forth by hand, the assistant works with Featuriq MCP directly.