I gave my coding agent a sense of taste. It picks restaurants from my music

Описание и применение

Кодовый агент теперь может рекомендовать рестораны, соответствующие музыкальным предпочтениям пользователя, используя API Qloo и собственный MCP-сервер.

Задача

A coding agent can refactor code but cannot recommend restaurants based on personal taste.

Результат

The agent can now suggest restaurants that match the user’s music and film tastes, enabling personalized date planning.

Рабочий процесс

• 1. Install and run the qloo-taste-mcp server with a QLOO_API_KEY.
• 2. Configure the agent’s .mcp.json to point to the server.
• 3. Use the provided tools (qloo_search, qloo_recommend, qloo_trending, qloo_tags) to query the taste graph.
• 4. The agent performs a cross-domain search: e.g., "I love Taylor Swift and Wes Anderson films — find me a restaurant in LA" → qloo_search for seeds → qloo_recommend for restaurants.

Доказательства

Qloo's Taste API sits on 250M+ cultural entities — music, movies, restaurants, brands, destinations — with a graph of how tastes connect.

Источник

I built qloo-taste-mcp: an MCP server that hands all of that to any agent as four tools.

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Zero third-party dependencies — Python stdlib only. Without an API key the tools return a helpful error instead of crashing.

Источник

There's also a live demo in demo/ — Taste Match, a tiny web app where you search things you like, pick a category, and get recommendations.

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