经 AI Skill Hub 精选评估,Rust性能分析 获评「强烈推荐」。已获得 1.5k 颗 GitHub Star,这款MCP工具在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 8.0 分,适合有一定技术背景的用户使用。
Rust性能分析 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
Rust性能分析 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/pawurb/hotpath-rs
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"rust----": {
"command": "npx",
"args": ["-y", "hotpath-rs"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 Rust性能分析 执行以下任务... Claude: [自动调用 Rust性能分析 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"rust____": {
"command": "npx",
"args": ["-y", "hotpath-rs"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
A simple Rust profiler that shows exactly why your code is slow.
Performance feedback for developers and coding agents. Find CPU, memory, async, SQL, I/O and contention bottlenecks with a single Rust profiler. Export metrics to Prometheus and Grafana when you need production monitoring.
Try the TUI demo via SSH - no installation required:
ssh demo.hotpath.rs
Or let your AI coding agent set up profiling, paste into Claude Code, Codex, OpenCode or Cursor:
Set up hotpath-rs profiling in this Rust project using the instructions at https://hotpath.rs/agent/setup
hotpath-rs is an easy-to-configure Rust performance profiling toolkit that shows exactly where your code spends time, burns CPU, and allocates memory.
It helps you distinguish between functions that are slow because they wait on I/O and those that are CPU-intensive. Instrument functions, channels, futures, streams, SQL queries, HTTP calls, and byte-level I/O to find bottlenecks and focus optimizations where they matter most. Get actionable insights into time, memory, and async data flow with minimal setup.
Explore the full documentation at hotpath.rs. See CONTRIBUTING.md for development setup and guidelines.
You can use it to produce one-off performance (timing, memory or CPU) reports:

correlate all performance signals in Grafana with minimal profiling overhead:

catch performance regressions on every pull request with a simple CI integration:

inspect throughput and latency of network, file or compression I/O streams:

analyze SQL/HTTP calls performance with automatic source function attribution:

monitor throughput, performance and max queue depth of instrumented channels:

or use the live TUI dashboard to monitor real-time performance and async data flow metrics with debug info:
https://github.com/user-attachments/assets/2e890417-2b43-4b1b-8657-a5ef3b458153
The quickest way to set up hotpath is to let your own AI coding agent do it. Paste this prompt into Claude Code, Codex, OpenCode, Cursor or any other coding agent, inside your project repo:
Set up hotpath-rs profiling in this Rust project using the instructions at https://hotpath.rs/agent/setup
The agent inspects your project, including workspaces and any existing hotpath setup, adds the feature-gated dependency, instruments main and a small starting set of hot functions, channels and locks, and verifies that everything compiles with profiling enabled and disabled. When the project has a workload that exits, it runs it and shows you the first report; it does not start long-running servers just to get one. It then explains how to run profiling and the live TUI. It finally asks once whether to optionally set up hotpath Cloud to benchmark pull requests in CI.
The prompt is a one-time project setup. The hotpath-rs agent skill is what stays: reusable hotpath knowledge your agent loads in every later session. To install it, for example in a repo that already uses hotpath, run:
npx skills add hotpath-rs/hotpath --skill hotpath-rs
The skill maps every hotpath task, from instrumenting a crate to fixing a regression flagged in CI, to the documentation page that covers it, so your agent fetches the current docs instead of relying on its memory. Your agent remains in control: you review and approve edits through its regular permission prompts.
Add to your Cargo.toml:
[dependencies]
hotpath = "0.29"
[features]
hotpath = ["hotpath/hotpath"]
hotpath-cpu = ["hotpath", "hotpath/hotpath-cpu"]
hotpath-callgrind = ["hotpath", "hotpath/hotpath-callgrind"]
hotpath-alloc = ["hotpath", "hotpath/hotpath-alloc"]
hotpath-prometheus = ["hotpath", "hotpath/hotpath-prometheus"]
This config ensures that the lib has no compile time or runtime overhead unless explicitly enabled via one of the hotpath features. Every profiling feature also enables hotpath, so a single flag like --features=hotpath-alloc is enough. All the lib dependencies are optional (i.e. not compiled) and all macros are noop unless profiling is enabled.
You'll need only #[hotpath::main] and #[hotpath::measure] macros to get started:
#[hotpath::measure]
fn sync_function(sleep: u64) {
std::thread::sleep(Duration::from_nanos(sleep));
let vec1 = vec![1, 2, 3];
std::hint::black_box(&vec1); // force mem allocation
}
#[hotpath::measure]
async fn async_function(sleep: u64) {
tokio::time::sleep(Duration::from_nanos(sleep)).await;
}
// When using with tokio, place the #[tokio::main] first
#[tokio::main]
#[hotpath::main]
async fn main() {
for i in 0..1000 {
sync_function(i);
async_function(i * 2).await;
hotpath::measure_block!("custom_block", {
std::thread::sleep(Duration::from_nanos(i * 3))
});
}
}
Now, run your program with hotpath (and optionally hotpath-alloc features):
cargo run --features='hotpath-alloc'
On exit it will print a report with timings, memory allocations and thread usage metrics:
[hotpath] 1.20s | timing, alloc, threads
timing - Function execution time metrics.
+------------------------------+-------+----------+----------+----------+---------+
| Function | Calls | Avg | P95 | Total | % Total |
+------------------------------+-------+----------+----------+----------+---------+
| docs_example::main | 1 | 1.20 s | 1.20 s | 1.20 s | 100.00% |
+------------------------------+-------+----------+----------+----------+---------+
| docs_example::async_function | 1,000 | 1.16 ms | 1.24 ms | 1.16 s | 96.84% |
+------------------------------+-------+----------+----------+----------+---------+
| custom_block | 1,000 | 16.86 µs | 29.50 µs | 16.86 ms | 1.41% |
+------------------------------+-------+----------+----------+----------+---------+
| docs_example::sync_function | 1,000 | 12.16 µs | 23.84 µs | 12.17 ms | 1.02% |
+------------------------------+-------+----------+----------+----------+---------+
alloc-bytes - Exclusive allocation bytes by each function (excluding nested calls).
Total: 100.1 KB
+------------------------------+-------+---------+---------+---------+---------+
| Function | Calls | Avg | P95 | Total | % Total |
+------------------------------+-------+---------+---------+---------+---------+
| docs_example::main | 1 | 88.4 KB | 88.4 KB | 88.4 KB | 88.29% |
+------------------------------+-------+---------+---------+---------+---------+
| docs_example::sync_function | 1,000 | 12 B | 12 B | 11.7 KB | 11.71% |
+------------------------------+-------+---------+---------+---------+---------+
| custom_block | 1,000 | 0 B | 0 B | 0 B | 0.00% |
+------------------------------+-------+---------+---------+---------+---------+
| docs_example::async_function | 1,000 | 0 B | 0 B | 0 B | 0.00% |
+------------------------------+-------+---------+---------+---------+---------+
threads - Thread CPU and memory statistics. (Max RSS: 8.6 MB, Alloc: 108.5 KB, Dealloc: 123.8 KB, Diff: -15.3 KB, 5/15)
+-----------------+------+------+----------+---------+----------+
| Thread | Max% | Avg% | Alloc | Dealloc | Diff |
+-----------------+------+------+----------+---------+----------+
| main | 4.5% | 3.8% | 100.1 KB | 46.0 KB | 54.0 KB |
+-----------------+------+------+----------+---------+----------+
| tokio-rt-worker | 0.0% | 0.0% | 79 B | 70.1 KB | -70.0 KB |
+-----------------+------+------+----------+---------+----------+
| tokio-rt-worker | 0.0% | 0.0% | 4.2 KB | 471 B | 3.8 KB |
+-----------------+------+------+----------+---------+----------+
| tokio-rt-worker | 0.0% | 0.0% | 3.3 KB | 407 B | 2.9 KB |
+-----------------+------+------+----------+---------+----------+
| hp-functions | 1.3% | 1.0% | 0 B | 2.0 KB | -2.0 KB |
+-----------------+------+------+----------+---------+----------+
高效的Rust性能分析工具
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
AI Skill Hub 点评:Rust性能分析 的核心功能完整,质量优秀。对于Claude Desktop / Claude Code 用户来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。
| 原始名称 | hotpath-rs |
| 原始描述 | 开源MCP工具:Quickly find bottlenecks in Rust - one profiler for CPU, time, memory, and async。⭐1.5k · Rust |
| Topics | Rust性能分析调试 |
| GitHub | https://github.com/pawurb/hotpath-rs |
| License | MIT |
| 语言 | Rust |
收录时间:2026-05-27 · 更新时间:2026-05-30 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端