GenPark HyperLogLog Cardinality Estimator
LocalAbout GenPark HyperLogLog Cardinality Estimator
GenPark HyperLogLog Cardinality Estimator is a pure-Python estimator for distinct values in high-speed data streams. It is intended for streaming cardinality queries and uses only the Python standard library at runtime.
Key features
- 01Estimate distinct-value cardinality in streams
- 02Use 64 HyperLogLog registers
- 03Run with Python standard library only
- 04Serve as an MCP server or Python library
Use cases
- 01Estimate unique values in streaming queries
- 02Add cardinality estimation to Python code
- 03Expose estimation through an MCP server
MCP support
Runs as an MCP server and is described as directly usable by MCP clients, including Claude Desktop, Cursor, and LobeChat.
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 HyperLogLog Cardinality Estimator
Pure Python 64-register HyperLogLog cardinality estimator for high-speed streaming distinct query estimation.
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
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.