Monitoring and observability · MCP server
LLM Usage: MCP server, setup and security
LLM Usage is an open-source MCP server published on GitHub that lets an AI agent track LLM API spending and compare costs across supported model providers. Its repository has 5 stars and its last commit is from October 21, 2026 and it is licensed under MIT.
What LLM Usage does
According to its repository: “Local LLM API cost tracking for Anthropic, OpenAI, Qwen and DeepSeek: SQLite usage ledger, spend queries, provider comparison and CNY to USD conversion.”
The Model Context Protocol (MCP) is a standard plug between an AI agent and an outside service: once the server is connected, its tools show up in the agent, which decides when to call them. LLM Usage is listed under Monitoring and observability, a category of 57 servers in the directory, where it ranks #50 by stars.
Topics declared by the repository: deepseek, llm-costs, local-first, qwen, anthropic, cost-tracking.
Installing and connecting LLM Usage
Distribution modes declared in the official MCP registry:
- PyPI package (Python, started with
uvxorpip)
It works with any MCP-compatible client (Claude Desktop, Claude Code, Cursor, VS Code, Codex and most recent agents). You add one entry to the client's MCP configuration: server name, start command or URL, environment variables. Project language: Python. Listed systems: macOS, Windows, Linux.
Security: what to check
Connecting an MCP server means installing software that talks straight to your agent: it runs with your rights and the model trusts its tool descriptions. The risk therefore depends on what it can touch and how far its code can be trusted.
None of the risk signals we track (system access, secrets, missing licence, abandonment) applies to LLM Usage. That does not replace reading the code.
Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.
Project activity and maintenance
Created on May 4, 2026, the zhaoyue722/llm-usage-mcp repository has 5 stars and 0 forks, with 0 open issues. It has more stars than 13% of the servers in the directory. It received changes this month. Version published in the registry: 0.1.3 (August 3, 2026).
Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.
Verdict
LLM Usage is a reasonable choice; check the points below before connecting it.
Strengths
- Active project: recent commit
- Listed in the official MCP registry
- Permissive licence (MIT)
Limitations
- Few visible users
Alternatives
- Netdata81.1K ★
- MCP grafana3.6K ★
- Openstatus9.2K ★
- OneUptime7.8K ★
- Homebutler305 ★
- SolidPing26 ★
Learn more
Frequently asked questions
How do I install LLM Usage?
LLM Usage is distributed as PyPI package (Python, started with uvx or pip). Add it to your client's MCP configuration (Claude Desktop, Cursor, VS Code…) following the README of zhaoyue722/llm-usage-mcp.
Is LLM Usage free?
The code is published under the MIT licence and can be used free of charge.
Is LLM Usage safe?
No risk signal found (system access, secrets, licence, abandonment). As with any MCP server, keep rights minimal and read the code before sensitive use.
Is it an official server?
Nothing says so: it is a project published by zhaoyue722 on GitHub. Check who maintains it before giving it access.
What are the alternatives to LLM Usage?
In the same category: Netdata, MCP grafana, Openstatus, and OneUptime. Netdata and MCP grafana score higher on our grid.
Related articles
- Build your own MCP server in 30 minutesOctober 24, 2026
- Automate your inbox with an AI agentOctober 18, 2026
- MCP, A2A, and tool calling: agent protocols explainedOctober 17, 2026
- Build an AI agent with n8n, without codingOctober 16, 2026
Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score