Mirobody
Lab results to LOINC codes and UCUM units, symptoms and diagnoses to ICPC-3. Offline, no API key.
作者提供的说明(英文原文)
Server operations MCP: HTTP health checks, log analysis, deploy dry-runs, mail diagnostics.
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": {
"ellmos-servercommander": {
"command": "npx",
"args": [
"-y",
"ellmos-servercommander-mcp"
]
}
}
}在终端中运行这条命令
claude mcp add ellmos-servercommander -- npx -y ellmos-servercommander-mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"ellmos-servercommander": {
"command": "npx",
"args": [
"-y",
"ellmos-servercommander-mcp"
]
}
}
}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": {
"ellmos-servercommander": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"ellmos-servercommander-mcp"
]
}
}
}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8日
注册名称已通过 GitHub 账户 ellmos-ai 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(0.1.0-alpha.20)于14天前进入注册中心。
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:Mirobody, FHIR MCP Server, Google Health MCP
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:↓ 每周下载量i★ 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
Lab results to LOINC codes and UCUM units, symptoms and diagnoses to ICPC-3. Offline, no API key.
MCP server providing seamless access to FHIR APIs for AI tools and healthcare applications
Privacy-first unofficial Google Health API v4 MCP server for health, sleep, activity, HR agents
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。
Alpha Model Context Protocol (MCP) server for local-first server operations: deployment dry-runs, mail configuration status, access-log analysis, and resilient HTTP health checks.
German README: README_de.md
*Part of the ellmos-ai family under the open-bricks open-source umbrella.*
-blue.svg)
[!NOTE]
Discoverability & AI Search: Published on npm as
ellmos-servercommander-mcp, cataloged for MCP ecosystems inserver.json,glama.json, andsmithery.yaml, and indexed for AI/LLM search inllms.txt.
ellmos-servercommander-mcp is an authoritative, local-first Model Context Protocol (MCP) server engineered specifically for AI coding assistants and autonomous agent platforms (Claude Code, Cursor, Codex, Antigravity, Gemini). It enables agents to safely diagnose server health, analyze web server access logs, inspect mail readiness, and build dry-run deployment plans without exposing production infrastructure to unverified, destructive mutations or arbitrary shell execution.
Every operation is governed by strict local-first and zero-elevation guarantees:
The following diagram illustrates the decoupled layers of ServerCommander, from MCP host transport and Node.js process supervision to Python dispatching, operational engines, and local persistence sinks:
flowchart TD
subgraph HostLayer ["1. MCP Host & AI Client Layer"] Host["MCP Host: Claude Desktop / Claude Code / Cursor"] end
subgraph GatewayLayer ["2. Gateway & Process Supervision Layer"] NodeWrapper["Node.js CLI Wrapper (bin/ellmos-servercommander.js)"] end
subgraph CoreLayer ["3. Python MCP Server Core Layer"] FastMCP["Python MCP Server (FastMCP Transport stdio)"] Dispatcher["Tool Dispatcher & Parameter Validator"] i18nEngine["i18n Translation Engine (en, de, es, zh, ja, ru)"] end
subgraph OperationsLayer ["4. Operations & Diagnostics Engines"] HTTPProbe["HTTP Health Probe (sc_health_check)"] LogAnalyzer["Apache/Nginx Log Analyzer (sc_logs_analyze)"] DeployStaging["Deployment Staging & Manifest Planner (sc_deploy / sc_deploy_status)"] MailDiagnostics["IMAP/SMTP Safety Diagnostics (sc_mail_*)"] end
subgraph SinkLayer ["5. Local Storage & Audit Sink Layer"] SQLiteHist[("Local SQLite Deploy History (deploy-history.db)")] JSONReports[("Sanitized JSON Log Reports")] AuditSink["Local Diagnostic Outputs & Stdout Stream"] end
Host <-->|"stdio / JSON-RPC"| NodeWrapper NodeWrapper <-->|"Child Process Stdio"| FastMCP FastMCP --> Dispatcher Dispatcher <--> i18nEngine Dispatcher --> HTTPProbe Dispatcher --> LogAnalyzer Dispatcher --> DeployStaging Dispatcher --> MailDiagnostics DeployStaging -.->|"Optional opt-in persist"| SQLiteHist LogAnalyzer -.->|"Optional persist_report"| JSONReports HTTPProbe -.-> AuditSink MailDiagnostics -.-> AuditSink
========================================================================================
ellmos-servercommander-mcp: Four-View Architectural Topology ========================================================================================
[VIEW 1: COMPONENT STRUCTURE & TRANSPORT BOUNDARIES] +----------------------------------------------------------------------------------+ | 1. MCP Host & AI Client (Claude Desktop / Claude Code / Cursor / Codex) | | - Stdio JSON-RPC transport protocol | +----------------------------------------------------------------------------------+ | | (JSON-RPC via stdio stream) v +----------------------------------------------------------------------------------+ | 2. Gateway & Process Supervision (bin/ellmos-servercommander.js) | | - Node.js CLI launcher & process supervisor (update-notifier checks) | | - Working directory isolation: PYTHONSAFEPATH=1 prevents rogue import hijack | +----------------------------------------------------------------------------------+ | | (Supervised child process stdio) v +----------------------------------------------------------------------------------+ | 3. FastMCP Dispatcher & i18n Localization Engine (src/servercommander/) | | - FastMCP server lifecycle, tool schema generation & parameter validation | | - Multi-language localization engine (6 locales: en, de, es, zh, ja, ru) | +----------------------------------------------------------------------------------+
[VIEW 2: OPERATIONAL ENGINES & DRY-RUN EXECUTION] +----------------------------------------------------------------------------------+ | 4. Tool Handlers & Diagnostic Engines | | - sc_health_check: Non-blocking HTTP/HTTPS probe with timeout & batch error | | - sc_logs_analyze: Sanitized Apache/Nginx access-log parser & threat detector| | - sc_deploy / sc_deploy_status: Fail-safe SHA-256 tree hashing & dry-run plan| | - sc_mail_list / sc_mail_read / sc_mail_send: Dry-run IMAP/SMTP diagnostics | +----------------------------------------------------------------------------------+
[VIEW 3: LOCAL PERSISTENCE & CONCURRENCY MODEL] +----------------------------------------------------------------------------------+ | 5. Local Storage & Audit Sinks | | - Local SQLite Deploy History (deploy-history.db) with explicit lease release | | - Sanitized JSON diagnostic reports (optional persist_report) | | - Thread-isolated HTTP GET workers preventing event-loop stalling | +----------------------------------------------------------------------------------+
[VIEW 4: SECURITY BOUNDARY & ZERO-EGRESS PERIMETER] +----------------------------------------------------------------------------------+ | 6. Governance & Defense Invariants (INV-LOCAL-01 through INV-SLA-10) | | - RunAsInvoker: 100% unprivileged user mode; zero root/sudo/UAC requirement | | - Zero-Egress by default: zero telemetry, zero unverified background outbound | | - Safe Process Isolation: cwd package exclusion, sanitized error messages | | - 48h Response SLA & statutory liability limitation (§ 521 BGB) | +----------------------------------------------------------------------------------+ ========================================================================================
The following sequence diagram demonstrates the lifecycle of operations dispatched by an AI agent through ServerCommander, showing concurrent HTTP probing, log parsing, and dry-run manifest calculation:
sequenceDiagram
autonumber actor User as AI Assistant / User participant Host as MCP Host (Claude / Cursor) participant Wrapper as Node.j
只显示了开头部分;其余内容请在仓库中阅读