Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@taste-layer what should our group do Saturday? Ana likes museums, Ben likes hiking, Cara likes live music"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Agents are good at reasoning and bad at taste. This project gives any agent a taste layer built on the Qloo taste graph, as:
An MCP server (mcp/server.ts) with two tools: resolve_taste_anchor and recommend_for_group.
A hosted demo (Next.js) that answers the same question twice, once from a plain LLM and once grounded in Qloo, side by side.
The key idea is group coverage ranking. Qloo is queried once per person from what that person likes, plus once for the whole group. Picks are ranked by coverage (how many people the pick fits), then affinity. A plain LLM has no per-person signal, only stereotypes.
Built for the Qloo Agentic Hackathon 2026. Without QLOO_API_KEY the app runs in clearly labeled SAMPLE DATA mode (local fixtures, not Qloo output) so it can be tested offline. With a key it calls Qloo's /search and /v2/insights endpoints on the hackathon base URL.
Related MCP server: agentic-travel-recs
cp .env.example .env.local # add QLOO_API_KEY (hackathon key), optional LLM_API_KEY
npm install
npm run dev # demo at http://localhost:3000
npm test # unit tests
npm run mcp # MCP server over stdioMCP client config:
{ "mcpServers": { "taste-layer": { "command": "npx", "args": ["tsx", "mcp/server.ts"], "env": { "QLOO_API_KEY": "..." } } } }src/lib/qloo.ts Qloo client (search + insights, X-Api-Key header)
src/lib/taste.ts anchor resolution and group coverage ranking
src/lib/plan.ts runs the with/without-Qloo comparison
src/lib/sample.ts offline fixtures (sample mode only)
mcp/server.ts MCP server
MIT
This server cannot be deployed
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