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GenPark Concurrency Semaphore Guard

GenPark Concurrency Semaphore Guard

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genpark.aiAI & Agents7Updated 1w ago
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About GenPark Concurrency Semaphore Guard

GenPark Concurrency Semaphore Guard is an in-memory concurrency guard for downstream LLM APIs. It combines a leaky bucket with a fair semaphore to help limit rate-limit saturation.

Key features

  1. 01In-memory concurrency control
  2. 02Leaky-bucket rate limiting
  3. 03Fair semaphore guard
  4. 04Uses only the Python standard library

Use cases

  1. 01Control concurrent calls to downstream LLM APIs
  2. 02Reduce rate-limit saturation in agent workflows

MCP support

Run `python mcp_server.py` to expose the concurrency guard as an MCP server.

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-concurrency-semaphore-guard-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 Concurrency Semaphore Guard

License: MIT Python Standard Library Model Context Protocol GenPark

In-memory concurrency leaky-bucket and fair semaphore guard protecting downstream LLM APIs from rate-limit saturation.

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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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.