GenPark Reservoir Sampling Stream Auditor
LocalAbout GenPark Reservoir Sampling Stream Auditor
GenPark Reservoir Sampling Stream Auditor is a Python engine for unbiased reservoir sampling and quantile estimation in LLM telemetry and high-volume token streams. It is aimed at stream-processing workflows and runs as an MCP server or Python module, using only the standard library.
Key features
- 01Unbiased reservoir sampling
- 02Quantile estimation
- 03O(k) memory use
- 04Python standard library only
Use cases
- 01Sampling high-volume token streams
- 02Estimating quantiles in LLM telemetry
MCP support
It implements the MCP protocol as a native server. MCP clients can use its stream sampling and quantile estimation capabilities.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEGenPark Reservoir Sampling Stream Auditor
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O(k) space unbiased stream reservoir sampling and quantile estimation engine for LLM telemetry and high-volume token streams.
Features
- 100% Python Standard Library: Zero external pip dependencies. Completely self-contained and portable.
- Enterprise Grade: Designed for mission-critical AI Agent workflows and LLM tool calling.
- Dual MCP Protocol Compliance: Native Model Context Protocol (MCP) server implementation.
- Microsecond Latency: Pure in-memory deterministic algorithms.
Installation & Usage
As a Python Module
from client import *
Run MCP Server
python mcp_server.py
Community & Verification
- Organization: alphaparkinc
- Homepage: https://www.genpark.ai
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.