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GenPark Sliding Window Rate Anomaly Detector

GenPark Sliding Window Rate Anomaly Detector

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genpark.aiData & Analytics7Updated 1w ago
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About GenPark Sliding Window Rate Anomaly Detector

A streaming detector for anomalies and bursts in LLM token and API rates, using sliding windows, z-scores, and EWMA. It is intended for monitoring rate spikes and can run as an MCP server or a library.

Key features

  1. 01Streaming sliding-window detection
  2. 02Z-score anomaly detection
  3. 03EWMA-based detection
  4. 04LLM token and API rate monitoring
  5. 05Python standard library only

Use cases

  1. 01Detecting spikes in LLM token rates
  2. 02Flagging bursts in API request rates

MCP support

Runs as an MCP server, making its rate anomaly detection available to MCP clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository
Packages
mcpbgenpark-sliding-window-rate-anomaly-detector-skill-v1.0.1.mcpb

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

GenPark Sliding Window Rate Anomaly Detector

License: MIT Python Standard Library Model Context Protocol GenPark

Real-time streaming sliding-window anomaly and burst detector using z-score and EWMA for LLM token and API spikes.

Part of the GenPark AI Agent Skills Ecosystem.

🌟 Key Features

  • 100% Python Standard Library: Zero third-party runtime dependencies.
  • MCP Native: Plugs directly into Claude Desktop, Cursor, LobeChat, and autonomous agent DAGs.
  • Production Resilience: Strict schema conformance, zero data loss, enterprise hardened.

🚀 Installation & Usage

Run as an MCP server:

python mcp_server.py

Use directly as a library:

from client import *

See example_usage.py for comprehensive integration demonstrations.

📄 License

MIT License. Maintained by GenPark AI.

Comments

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