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GenPark LLM Token Cost Budget Tracker

GenPark LLM Token Cost Budget Tracker

Local
genpark.aiAI & Agents7Updated 1w ago

About GenPark LLM Token Cost Budget Tracker

GenPark LLM Token Cost Budget Tracker is a Python MCP server and library for tracking token costs across multiple models. It maintains budgets, forecasts spending rates, enforces limits, and issues early warnings.

Key features

  1. 01多模型令牌成本账本
  2. 02支出速率预测
  3. 03预算上限控制
  4. 04提前预警
  5. 05仅使用 Python 标准库

Use cases

  1. 01跟踪多个模型的令牌成本
  2. 02预测预算消耗速度
  3. 03设置支出限额并接收预警

MCP support

作为 MCP 服务运行,向 MCP 客户端提供多模型令牌成本跟踪、预算管理和支出预测功能。

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-llm-token-cost-budget-tracker-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 LLM Token Cost Budget Tracker

License: MIT Python Standard Library Model Context Protocol GenPark

Multi-model LLM token cost budget ledger and burn rate forecaster enforcing spending limits and early warning alerts.

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.