LLM Usage MCP server

<!-- mcp-name: io.github.zhaoyue722/llm-usage-mcp -->

llm-usage-mcp

LLM Usage &amp; Cost Tracker — your local-first spend watchdog

English | 中文

Stop treating your LLM API bills like a scary horror movie you only look at through your fingers at the end of the month. Know what your LLM calls actually cost — across every provider, in one place, on your own machine. Ask your coding agent (MCP) or type a command (CLI).

It's a cost meter, not a router: it tells you what you spent and which provider fits a workload — it never changes your calls. Pairs happily alongside a router or a model-leaderboard tool.

Or straight from the terminal — your week's spend, broken down by provider, and a cross-provider cost comparison before you commit to a model:

Why you'd want this

You're calling LLMs from a handful of providers — Claude, GPT, plus Chinese models like Qwen and DeepSeek. Each one bills in its own dashboard, in its own currency, with its own rules for what a "cached token" costs. So the simplest possible question — how much am I spending, and on what? — turns into four browser logins, looking up exchange rates for RMB to USD, and trying to decipher what a "cached context token discount" actually means in midnight math. Most people just cross their fingers and let the bill be a surprise at the end of the month.

llm-usage-mcp captures every call you make into one local store, costs it correctly per provider at the moment it happens, and hands the answer back two ways:

• Ask your coding agent. It's an MCP server, so Claude Code, Cursor, or any MCP client can answer "how much did I spend on Claude this week?" or "which provider is cheapest for a 10k-in / 2k-out call?" in plain English.
• Or type a command. It's also a CLI — llm-usage spend, llm-usage compare, llm-usage recommend — for when you'd rather not round-trip through an agent.

And it stays out of your way:

• Local-first. No SaaS, no signup, no telemetry. Just a SQLite file at ~/.llm-usage/usage.db. Privacy is a feature, not a setting.
• Multi-provider, Chinese models included. Anthropic, OpenAI, DeepSeek, Qwen — streaming and non-streaming for all four. DeepSeek and Qwen run the same capture path as Anthropic and OpenAI, not a bolted-on afterthought. More providers (Gemini, Bedrock, Moonshot, …) are on the way.

Quickstart

Two minutes from git clone to your first captured call. This part is about capture — getting calls recorded. Reading the data back comes next.

1. Install

Shortened. The full README is on GitHub.