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., "@Personal Agent Biometric Sleep & Readiness Workload Allocator My sleep was 5.2h with 0.8h deep/REM and HRV 72%; reschedule deep work today."
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.
Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)
Personal Agent Biometric Sleep & Readiness Workload Allocator. Ingests sleep debt, REM/Deep ratios, and HRV recovery metrics to calculate an objective cognitive readiness index and dynamically reschedule high-intensity deep work blocks.
GenPark Official Website: https://www.genpark.ai
LinkedIn Official Page: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)
Related MCP server: genpark-personal-sleep-recovery-chronotype-coach-skill
100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.
Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.
from client import PersonalAgentBiometricWorkloadAllocator
def main():
allocator = PersonalAgentBiometricWorkloadAllocator()
bio = {"sleep_hours": 5.2, "deep_rem_hours": 0.8, "hrv_baseline_ratio": 0.72}
tasks = [
{"name": "System Architecture Review", "cognitive_demand": "HIGH", "duration_mins": 90},
{"name": "Database Sharding Spec", "cognitive_demand": "HIGH", "duration_mins": 60},
{"name": "Team Standup", "cognitive_demand": "LOW", "duration_mins": 15}
]
res = allocator.allocate_cognitive_workload(bio, tasks)
print("--- Biometric Workload Allocation ---")
print(f"Readiness Score: {res['readiness_score']} | Tier: {res['capacity_tier']}")
print(f"Advice: {res['advice']}")
for t in res['tasks_schedule']:
print(f"* [{t['schedule_status']}] {t['name']} ({t['duration_mins']}m, {t['cognitive_demand']})")
if __name__ == "__main__":
main()
Start standard JSON-RPC 2.0 server over stdio:
python mcp_server.pyExecute embedded test harness:
python mcp_server.py --testInspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.
Licensed under the MIT License. Copyright © 2026 GenPark AI. Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550
This server cannot be deployed
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