MCP server for Featuriq — query and manage product feedback and roadmaps from any AI client
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.
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.
Once Featuriq MCP is connected, these are the calls the assistant has available:
Cursor — The Cursor tool exposed by this serverget_top_requests — Returns the top feature requests sorted by vote count or revenue impactsearch_feedback — Semantically searches all feedback posts using natural language — finds relevant results even when the exact words don't matchget_feature_feedback — Returns all comments and discussion for a specific feature requestget_prioritization — Returns an AI-prioritized list of features, scored across the factors you chooseupdate_feature_status — The update_feature_status tool exposed by this servernotify_requesters — Sends a personalized notification to every user who voted for a featurecreate_post — The create_post tool exposed by this serverThe 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.
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.
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We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| 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. |
{
"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.
| Variable | Description | Required |
|---|---|---|
| FEATURIQ_API_KEY | Credential the server authenticates with. | Yes |
| FEATURIQ_API_URL | Endpoint or connection string the server talks to. | Yes |
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