genpark-cognitive-load-budget-optimizer-skill
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., "@genpark-cognitive-load-budget-optimizer-skillcheck my current token usage and suggest optimizations"
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.
genpark-cognitive-load-budget-optimizer-skill
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
🌟 Overview
genpark-cognitive-load-budget-optimizer-skill delivers robust, industrial-grade capabilities for autonomous agents, copilot frameworks, and personal assistant architectures. Built exclusively on the Python standard library with zero external runtime dependencies, it integrates seamlessly as a native Model Context Protocol (MCP) server or an importable Python module.
Dynamic Cognitive Load, Latency & Token Budget Router. Monitors running agent loops, estimates token consumption and monetary cost, prunes verbose thinking chains when nearing limits, and routes sub-tasks between fast/cheap models and deep reasoning engines.
💡 Key Capabilities
Zero-Dependency Architecture: Runs anywhere Python 3.9+ is installed without
pip installoverhead or supply-chain vulnerabilities.Model Context Protocol (MCP) First: Compatible with Claude Desktop, Cursor, GenPark Engine, and custom agentic frameworks.
Deterministic & Safe: Designed with strict validation, graceful error handling, and structured telemetry.
High Concurrency & Low Latency: In-memory caching and optimized data structures for real-time agent execution loops.
Related MCP server: MCP Cost Tracker & Router
🚀 Quickstart
1. Direct Python Usage
from client import CognitiveLoadBudgetOptimizer
client = CognitiveLoadBudgetOptimizer()
result = client.optimize_cognitive_budget()
print(result)2. Standalone MCP Server Execution
Run the MCP server via standard JSON-RPC 2.0 stdio:
python mcp_server.pyVerify standard compliance and self-tests:
python mcp_server.py --test3. Claude Desktop / Cursor MCP Configuration
Add this tool to your claude_desktop_config.json or Cursor MCP settings:
{
"mcpServers": {
"genpark-cognitive-load-budget-optimizer-skill": {
"command": "python",
"args": ["/absolute/path/to/genpark-cognitive-load-budget-optimizer-skill/mcp_server.py"]
}
}
}🛠️ Verification & Testing
Run the included verification suite:
python example_usage.py📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
Developed with ❤️ by the GenPark Autonomous Agent Ecosystem Team.
This server cannot be deployed
Maintenance
Related MCP Connectors
Meter, cap, and block AI agent spend before the provider is charged.
Budget & cost control for AI agents — per-agent spend caps + rate limits before each call.
Enforce AI budgets before the model call and track cost per customer across 10 providers.
Agent Token Budget MCP — hard per-session token + spend cap with signed budget-exhausted
Related MCP Servers
- FlicenseNot gradedqualityDmaintenanceEnables AI agents to track LLM costs, enforce budgets, compare models, and estimate expenses through simple tool calls.-
- AlicenseNot gradedqualityBmaintenanceLocal-first cost tracking and model routing for MCP agents with offline token counting, budget alerts, and spend reports.34 npmMIT
- AlicenseNot gradedqualityDmaintenanceLLM cost optimization and provider usage visibility for MCP-capable agents.40 npmMIT
- AlicenseNot gradedqualityBmaintenanceEnables agents to track real-time token spend and cost velocity, automatically throttle or freeze execution when budgets are exceeded, and enforce sandbox and safety guardrails.7MIT