Llm Usage & Cost Tracker MCP Server

@zhaoyue722

リポジトリ

基本情報

ライセンス

MIT

トランスポート

stdio

作者

zhaoyue722

投稿者

越越的糖

Llm Usage & Cost Tracker MCP Server について

A local-first, multi-provider cost meter for LLM usage, exposed as MCP tools. Captures every call into a local SQLite ledger and lets any coding agent query spend, compare providers, and get recommendations — no cloud, no account. First-class support for Chinese providers (Qwen,

#cost-tracking #llm #observability #pricing #local-first #anthropic #openai #deepseek #qwen

Llm Usage & Cost Tracker MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "llm-usage": {
      "command": "uvx",
      "args": [
        "llm-usage-mcp"
      ]
    }
  }
}

Llm Usage & Cost Tracker MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。

概要

What is Llm Usage & Cost Tracker?

Llm Usage & Cost Tracker is a local-first MCP server and CLI that captures LLM API calls across multiple providers (Anthropic, OpenAI, DeepSeek, Qwen), calculates costs, and answers spend queries through an agent or terminal. It is a cost meter, not a router.

How to use Llm Usage & Cost Tracker?

Install via uv tool install llm-usage-mcp (or clone from source). Set at least one provider API key as an environment variable. Run the capture proxy (llm-usage-proxy), which listens on 127.0.0.1:5525. Point your LLM client’s base URL to the proxy. Use the MCP server (e.g., claude mcp add llm-usage -- uv run llm-usage-mcp) to ask agent questions, or run CLI commands like llm-usage spend, llm-usage compare, and llm-usage recommend.

Key features of Llm Usage & Cost Tracker

Local-first SQLite storage, no telemetry.
Supports Anthropic, OpenAI, DeepSeek, and Qwen.
MCP server with seven query tools.
CLI with subcommands for spend, compare, recommend.
Capture proxy logs all API calls idempotently.
Cross-provider cost comparison and recommendation.
Works with streaming and non-streaming calls.
No SaaS signup or account needed.

Use cases of Llm Usage & Cost Tracker

Monitor daily, weekly, or monthly spend per provider or model.
Compare hypothetical workload costs across all priced models.
Get the cheapest model recommendation for a budget and workload.
Integrate with Claude Code, Cursor, or any MCP client.
Manually log usage when the capture proxy is not in use.

よくある質問

What does it do vs a router?

It is a cost meter, not a router. It tells you what you spent and which provider fits a workload, but never changes your calls. It pairs happily alongside a router or a model-leaderboard tool.

What are the runtime requirements?

Python 3.13+, uv or pipx for installation. SQLite is built into Python, no extra database setup.

Where does my usage data live?

All data is stored locally in a SQLite file at `~/.llm-usage/usage.db`.

Which providers are supported?

Anthropic, OpenAI, DeepSeek, and Qwen are supported for both streaming and non-streaming. More providers (Gemini, Bedrock, Moonshot) are planned.

How do I authenticate and connect?

API keys are set as environment variables (e.g., `ANTHROPIC_API_KEY`). The proxy holds keys server-side; clients point their base URL at `http://127.0.0.1:5525` and never need keys directly.

マーケットプレイスの他のコンテンツ

Full Self Browsing

Lakshman Turlapati

DOM-first AI browser automation for Chrome and MCP clients.

194

fhirHydrant

faulkj

Open-source Node.js FHIR MCP server with SMART Backend Services, metadata-aware search/CRUD tools, compact responses, FHIRPath filtering, safe pagination, audit events, and terminology lookup.

Meticulous

Termany

thinkany-ai

Agent-Native Terminal

174

OpenLore

aakarim

Serve your docs to AI agents over SSH and MCP. Agents use ls, cat, grep, find, and pipes against a virtual filesystem backed by your documentation. Identity-scoped access, governed writing with human approval, and portab