genpark-agent-rate-limit-token-bucket-regulator-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-agent-rate-limit-token-bucket-regulator-skillCheck my OpenAI TPM budget and reserve 12k tokens for the next request"
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-agent-rate-limit-token-bucket-regulator-skill
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
📌 Overview & Capability
genpark-agent-rate-limit-token-bucket-regulator-skill is a deterministic, high-performance, zero-dependency Python tool and native Model Context Protocol (MCP) server designed for autonomous AI agents, multi-agent frameworks (LangGraph, CrewAI, AutoGen, OpenAI Swarm), and developer environments (Cursor, Windsurf, Claude Desktop).
Executive Capability: Multi-provider token-bucket and sliding-window rate limiter preventing TPM/RPM throttling across LLM APIs with predictive token consumption budgeting.
⚡ Key Highlights
🐍 Zero External
pipDependencies: Implemented entirely with pure Python standard library for instant zero-overhead execution.🔌 Native Model Context Protocol (MCP): Plugs directly into any MCP-compliant client via JSON-RPC 2.0 stdio.
⚡ Sub-Millisecond Execution: Slashes token burn and latency by resolving routine agent tasks deterministically without frontier LLM round-trips.
🛡️ Production-Hardened: Comprehensive error handling, boundary validation, and telemetry.
Related MCP server: 429-throttle-mcp
🏗️ Architecture
graph LR
Agent([🤖 Autonomous Agent / IDE]) -->|MCP Protocol / JSON-RPC| Server[⚡ genpark-agent-rate-limit-token-bucket-regulator-skill Server]
Server --> Core[🧠 Deterministic Processing Core]
Core --> Out[📊 Actionable Result & Telemetry]
Out --> Agent🚀 Quickstart & Usage
1. Direct Python Client Execution
python example_usage.py2. Programmatic Integration
from client import AgentRateLimitRegulator
client = AgentRateLimitRegulator()
result = client.run_rate_limiter_benchmark()
print(result)🔌 Model Context Protocol (MCP) Setup
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
claude_desktop_config.json
{
"mcpServers": {
"genpark-agent-rate-limit-token-bucket-regulator-skill": {
"command": "python",
"args": ["/path/to/genpark-agent-rate-limit-token-bucket-regulator-skill/mcp_server.py"]
}
}
}Direct MCP Testing
python mcp_server.py --test📊 Technical Specifications
Parameter | Type | Required | Description |
|
| Yes | Primary context, code, schema, or content input |
|
| No | Execution flags, compression ratios, or risk bounds |
This server cannot be deployed
Maintenance
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