The Culture Server

MCP server for The Culture — a fashion-focused social media app. Exposes Supabase database operations and Hugging Face model inference as MCP tools for use in Claude Code, n8n agents, and the Larry orchestrator.

Tools

DB Tools (Supabase)

Tool	Description
get_user_context	Profile + last 20 saves + last 20 likes with product_tags
get_post_context	Single post content, image_url, product_tags
get_trending_posts	Posts filtered by archetype, ordered by recency
write_message	Insert a DM into messages (used by Larry)
update_product_tags	Update a post's product_tags array
get_community_context	Community details, member count, recent posts

Model Tools (Hugging Face)

Tool	Model	Task
call_archetype_model	TheCulture-fashion-archetype-labeler	Image → style archetype
call_content_moderation	TheCulture-content-moderation-model	Text → safe/unsafe + sub_category
call_trend_forecaster	TheCulture-trend-forecasting-model	Tabular → trend lifecycle stage
call_ad_ctr_model	TheCulture-ad-ctr-model	Tabular → click probability
call_recommendation_engine	TheCulture-recommendation-engine	User + posts → ranked affinity scores

Running the server

This server uses the MCP Streamable HTTP transport (stateless mode). It listens on PORT (default 3000) and exposes:

• POST /mcp — MCP JSON-RPC endpoint
• GET /health — health check, returns {"status":"ok"}

npm install
npm run build
SUPABASE_URL=... SUPABASE_SERVICE_ROLE_KEY=... HF_TOKEN=... HF_USERNAME=Dc-4nderson npm start

For local development without a build step: npm run dev (uses tsx).

Deploying

Deploy anywhere that runs a Node HTTP server (Render, Railway, Fly.io, etc.). Set the same four env vars (SUPABASE_URL, SUPABASE_SERVICE_ROLE_KEY, HF_TOKEN, HF_USERNAME) in the platform's environment config, and make sure the platform's assigned PORT is respected (it is, via process.env.PORT).

Connecting a client (Claude Code / Claude Desktop)

Point the client at the deployed /mcp URL:

{
  "mcpServers": {
    "the-culture": {
      "url": "https://your-deployment.example.com/mcp"
    }
  }
}

Notes

• Models not yet deployed to HF Inference Endpoints will return a 503 until deployed from their training notebooks.
• CTR model default threshold is 0.3 (not 0.5) due to the platform's ~2.5% base click rate.
• Recommendation engine requires user_id and post_ids that exist in the model's training data.