← Top 100 Full directory (1,587)

Wellness Pulse

Communication Remote ★ 944 6 tools

Wellness Pulse MCP is a plug-and-play intelligence layer that transforms raw wellbeing data into clear, actionable insights for AI systems. With a single integration, your copilots, agents, and dashboards gain access to trusted public benchmarks (CDC PLACES) and live institutional wellness signals — no analyst required. 🔗 Website: https://wpulse.org 🧠 What It Does 📊 CDC mental health benchmarks by ZIP or county 🏢 Real-time institutional wellness trends & alerts 🗣️ Plain-English explanations alongside structured JSON 🔌 One integration for all AI workflows ⚡ Built For Healthcare teams, universities, HR platforms, and AI agents that need fast, reliable wellbeing intelligence. 🔒 Privacy First No PII. No tracking. Designed for compliance-conscious environments.

Connect

MCP endpoint

https://wellnesspulse--wellnesspulse.run.tools

How to connect

Smithery (hosted)

1. Open https://smithery.ai/servers/WellnessPulse/WellnessPulse
2. Click Connect and complete OAuth in your MCP client (Claude, Cursor, VS Code, etc.)
3. MCP endpoint: https://wellnesspulse--wellnesspulse.run.tools

Tools (6)

get_mental_health_benchmark
CDC PLACES frequent mental distress (FMD) benchmark. Inputs: zip (string) or county_fips (5-digit). If neither provided, returns national summary. Outputs: scope (county|national|national_fallback), core values (pct and CI), optional national_percentile_rank, and marketing_copy.
get_sector_snapshot
Sector-level wellness snapshot over a lookback window. Inputs: sector (string), window_days (number, default 90). Outputs: institutions_with_responses, total_responses, avg_wellness_score, note.
get_basic_alert_guidance
Default alert thresholds by org_size and location_type (pre-learning baseline). Inputs: org_size (small|mid|large), location_type (campus|ward|store|office|other). Outputs: recommended_drop_pct, window_days, min_responses, rationale.
get_institution_snapshot
Institution snapshot over a lookback window. Inputs: institution_id (number), window_days (default 30). Outputs: total_responses, avg_wellness_score, last_response_at.
get_institution_trend_daily
Daily average wellness trend for an institution. Inputs: institution_id (number), window_days (default 90). Outputs: series of { day, avg_wellness, responses } sorted ASC.
get_institution_alert_check
Alert check: compares last 7d vs prior 7d avg wellness for a drop. Inputs: institution_id (number), drop_threshold_pct (default 15). Outputs: averages {last7, prior7}, drop_pct, threshold_pct, alert (boolean).