Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
Cut 95% of framework noise and token bloat from error logs for AI agents.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"razor": {
"command": "npx",
"args": [
"-y",
"tokenectomy-razor"
]
}
}
}在终端中运行这条命令
claude mcp add razor -- npx -y tokenectomy-razor粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"razor": {
"command": "npx",
"args": [
"-y",
"tokenectomy-razor"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"razor": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"tokenectomy-razor"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 Tokenectomy-Labs 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(1.3.3)于2个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Spec Driven Development, Remote Desktop Commander, Draugr
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Security scanning for AI agents: SAST, SCA, secrets, IaC, DAST, ranked by real risk.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
Tokenectomy Razor
Fix Claude Code & Cursor Rate Limits: Zero-Latency Local AI Gateway & Context-Surgery MCP Server in Rust Stop burning 20,000 tokens on node_modules stack traces. Tokenectomy Razor is a sub-millisecond local AI Gateway reverse proxy (127.0.0.1:8080) & companion MCP server written in safe Rust. Excises 41.7%–99.7% framework noise from terminal error logs, redacts leaked DB credentials/JWTs, and caches prompts for free.
Quick Start (10s) • Architecture • The Problem • Before & After • Live Interactive Demo • Benchmarks
┌────────────────────────────────────────────────────────┐
│ Autonomous Agents │ │ Claude Code • Cursor • Antigravity • Cline • Windsurf │ └───────────────────────────┬────────────────────────────┘ │ ▼ (HTTP / SSE Outbound Prompts) ┌──────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ ⚡ TOKENECTOMY AI GATEWAY (127.0.0.1:8080) │ │ │ │ ┌─────────────────────────┐ ┌───────────────────────────┐ ┌──────────────────────────────────────────────┐ │ │ │ Zero-Cost Prompt │ │ Zero-Leak Credential │ │ Polyglot Trace Surgery │ │ │ │ Cache │ │ Redaction │ │ (9 Languages) │ │ │ │ <1ms Hit • 100% Free │ │ API Keys • JWTs • DB URIs│ │ node_modules • site-packages • .cargo │ │ │ └───────────┬─────────────┘ └─────────────┬─────────────┘ └──────────────────────┬───────────────────────┘ │ │ │ │ │ │ │ └───────────────────────────────┼────────────────────────────────────────┘ │ │ ▼ │ │ Smart Upstream Auto-Router │ │ (Anthropic • OpenAI • Groq • Ollama Local) │ └──────────────────────────────────────────────┬───────────────────────────────────────────────────────────────────┘ │ ┌────────────────────────────────┼────────────────────────────────┐ ▼ ▼ ▼ api.anthropic.com api.openai.com localhost:11434 (Claude 3.5 / 3.7 Sonnet) (GPT-4o / o1 / o3-mini) (DeepSeek / Llama)
wrap)Run any coding agent or test suite directly behind Tokenectomy's privacy & caching gateway with zero manual environment variable setup:
# Wrap Claude Code CLI
razor wrap -- claude
razor wrap -- aider --model deepseek/deepseek-chat
razor wrap npm test
npx -y tokenectomy-razor --proxy
💡 Smart Multi-Provider Auto-Routing & Universal LLM Transpiler ON by default! Tokenectomy automatically analyzes request paths and headers. Need to run Claude Code against DeepSeek or Ollama? Tokenectomy transparently transpiles Anthropic
/v1/messagesformat into OpenAI/v1/chat/completionson-the-fly and vice-versa.
Point your coding agent or CLI to the local gateway on 127.0.0.1:8080:
| Agent / Environment | Setup Command or Configuration | Auto-Routed Upstream |
|---|---|---|
| Claude Code | export ANTHROPIC_BASE_URL="http://127.0.0.1:8080" | https://api.anthropic.com |
| Cursor | Models > OpenAI Base URL: http://127.0.0.1:8080/v1 | https://api.openai.com |
| Google Antigravity / Gemini | export OPENAI_BASE_URL="http://127.0.0.1:8080/v1" | https://api.openai.com |
| Cline / Roo Code | Provider: OpenAI Compatible • Base URL: http://127.0.0.1:8080/v1 | https://api.openai.com |
| Ollama / Local LLMs | Base URL: http://127.0.0.1:8080 | http://localhost:11434 |
Open `http://127.0.0.1:8080/dashboard` in your browser to observe live token reductions, prompt cache hits, dollar savings, and redacted credentials in real time.
For agents calling deep diagnostic tools (get_error_context, analyze_code, apply_code_patch):
claude mcp add tokenectomy npx -y tokenectomy-razor --mcp
agy mcp add tokenectomy-razor -- npx -y tokenectomy-razor --mcp
.cursor/mcp.json){
"mcpServers": { "tokenectomy": { "command": "npx", "args": ["-y", "tokenectomy-razor", "--mcp"] } } }
claude_desktop_config.json){
"mcpServers": { "tokenectomy": { "command": "npx", "args": ["-y", "tokenectomy-razor", "--mcp"] } } }
npm test 2>&1 | npx tokenectomy-razor
When your app crashes during development (Next.js, Express, FastAPI, Tokio), the runtime dumps hundreds of lines of third-party plumbing from node_modules or site-packages.
When you paste that raw crash dump into Cursor or Claude:
node_modules/express/lib/router/layer.js or starlette/routing.py) and tries to edit library files instead of your actual application code.Raw Terminal Crash (45,820 tokens + Leaked Secrets)
│ ▼ <0.2ms Local Rust DFA Engine [Redact Passwords & Keys] ──► [Strip Third-Party Framework Frames] ──► [Isolate Root Cause] │ ▼ Clean Context (118 tokens • Zero Secrets • Sub-millisecond)
TypeError: Cannot read properties of undefined (reading 'token')
at loadComponents (/app/node_modules/next/dist/server/load-components.js:14:2) at renderToHTML (/app/node_modules/next/dist/server/render.js:50:5) at nextServer (/app/node_modules/next/dist/server/next-server.js:80:12) at processTicksAndRejections (node:internal/process/task_queues:95:5) at runMicrotasks (node:internal/process/task_queues:120:3) at checkoutHandler (/app/pages/api/checkout.ts:42:15) Database connection failed: postgresql://admin:super_secret_password@db.prod.internal:5432/primary API key leaked: [PopNx MCP ตัดค่าที่หน้าตาเหมือนกุญแจออก]
> **What Claude does:** Analyzes `load-components.js` and `next-server.js`, speculates on Webpack / Next.js internals, and leaks connection credentials to remote inference logs.
// [Tokenectomy Surgery: 5 internal framework frames pruned (83.3%)]
// Source: /app/pages/api/checkout.ts:42:15 42 | const sessionToken = req.headers.authorization.token; | ^ TypeError: Cannot read properties of undefined (reading 'token') 🛡️ [CONNECTION_STRING_REDACTED] 🛡️ [ANTHROPIC_KEY_REDACTED]
> **What Claude does:** Instantly identifies that line 42 in `checkout.ts` attempted to access `.token` on undefined headers. Suggests optional chaining `req.headers.authorization?.token` immediately. Credentials redacted before transmission.
All performance claims are hardware-grounded and independently reproducible on physical hardware (measured on 10-Core Intel Core i5-1235U @ 15W running Arch Linux, Kernel 6.13):
Hardware Dependency Notice: Performance is hardware-dependent; reported throughput represents measured results on the specified test hardware (10-Core Intel Core i5-1235U @ 15W TDP). Throughput scales with higher TDP desktop/server CPUs and faster memory buses. Developers are encouraged to independently audit performance using the reproduction command below.
📋 Click to expand full raw benchmark terminal log ($ cargo test --release)
$ cargo test --release --test stress_benchmark -- --nocapture
===================================================================================== 🧪 TOKENECTOMY OSS VERIFIABLE HEAVY STRESS BENCHMARK (100% REPRODUCIBLE IN OSS) Hardware: 10-Core / 12-Thread Intel Core i5-1235U | OS: Arch Linux | Kernel Telemetry Active Initial Baseline Process Memory (VmRSS): 3.45 MB =====================================================================================
🔥 [TEST 1/3] QUARTER-MILLION LINES LOG REDACTION TORTURE (250,0
只显示了开头部分;其余内容请在仓库中阅读