原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Mercury Spec Ops

作者:n0zer0d4y版本 0.1.2i★ –i在你的电脑上运行
注册名称io.github.n0zer0d4y/mercury-spec-ops

安装

概览

作者提供的说明(英文原文)

Modular MCP server for dynamic prompt generation and software development analysis tools

PopNx MCP 的三大支柱

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。

它能帮你做什么

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。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 注册中心没有列出需要填写的密钥。

你需要

  • npm已安装 Node.js(用于 npx 命令)

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": {
    "mercury-spec-ops": {
      "command": "npx",
      "args": [
        "-y",
        "@n0zer0d4y/mercury-spec-ops"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add mercury-spec-ops -- npx -y @n0zer0d4y/mercury-spec-ops

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "mercury-spec-ops": {
      "command": "npx",
      "args": [
        "-y",
        "@n0zer0d4y/mercury-spec-ops"
      ]
    }
  }
}

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。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "mercury-spec-ops": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@n0zer0d4y/mercury-spec-ops"
      ]
    }
  }
}

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。

它能做什么

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。

基础检查i

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

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 n0zer0d4y 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    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“搜索与 AI 记忆”中的其他服务器比较i

  • 星标数多于此分类中其他 5672 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 5672 个服务器的 1% 更新
  • 基础检查结果等于或优于此分类中其他 5672 个服务器的 100%

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。

常见问题

Mercury Spec Ops 安全吗?

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。

它需要哪些密钥或代码?

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。

它在我的电脑上运行还是在云端?

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)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.1.2)于12个月前进入注册中心。

哪些应用可以使用它?

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。

有什么可以替代它?

同一分类中有:Context7, Codebase Memory, Exa

替代选择

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

Context7

作者:upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT昨天更新
安装

Codebase Memory

作者:DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT21天前更新
安装

Exa

作者:exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT2个月前更新
安装

作者的 README

打开 README

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。

Mercury Spec Ops MCP Server: Prompts and Resources as _Tools_

The first MCP server to expose dynamic, AI-invocable tools for prompt generation and template assembly.

Transform how AI assistants interact with specialized content. Instead of static prompts and resources, Mercury Spec Ops provides 6 programmable tools that enable AI to dynamically generate technology-specific prompts and comprehensive templates on-demand. Built on a sophisticated modular architecture with 31 technology stacks, 10 analysis dimensions, and 34 template sections—all accessible through simple tool calls.

Features

This MCP server provides a modular, extensible architecture with:

Prompts (Enhanced with Enum Support)
  1. PRD Prompt - Generate Product Requirements Document with modular technology-specific analysis
  2. Codebase Analysis Prompt - Analyze codebases with modular technology/focus-specific analysis
  3. Bug Analysis Prompt - Analyze bugs with modular severity/technology-specific analysis
Resources (Modular Templates)
  1. PRD Template - Modular PRD template with technology-specific sections
  2. Codebase Analysis Template - Modular codebase analysis template with technology/focus-specific sections
  3. Bug Analysis Template - Modular bug analysis template with severity/technology-specific sections

Key Capabilities

Enum-Based Input Validation
  • Technology Stack (31 total):
  • Languages (11): JavaScript, TypeScript, Python, Java, Go, Rust, C#, PHP, Ruby, Swift, Kotlin
  • Runtimes (1): Node.js
  • Frontend (3): React, Angular, Vue
  • Backend (7): Express, NestJS, Django, Flask, Spring, Laravel, Rails
  • Databases (4): MongoDB, PostgreSQL, MySQL, Redis
  • Cloud (3): AWS, Azure, GCP
  • DevOps (2): Docker, Kubernetes
  • Analysis Focus (10 total): architecture, security, performance, testing, documentation, maintainability, scalability, reliability, code-quality, dependencies
  • Bug Severity (4 total): low, medium, high, critical
  • Target Audience: developers, business-users, enterprise, startup, healthcare, finance, and more
Programmatic Prompt Assembly
  • Base prompts + 31 technology modules + 10 focus modules + 4 severity modules
  • Automatic module selection based on input parameters
  • Priority-based module ordering
  • Multi-value support: Analyze multiple technologies and focus areas simultaneously
  • Comma-separated input: "node.js,react,postgresql" or "security,performance,code-quality"
  • Custom instruction integration
  • Intelligent fallback to base prompts
Modular Template System
  • Template sections with dependency resolution
  • Technology-specific template sections
  • Focus-specific template sections
  • Custom section support

Installation

Option 1: Local Development
  1. Clone and install dependencies:
git clone https://github.com/n0zer0d4y/mercury-spec-ops.git

cd mercury-spec-ops npm install npm run build

  1. Run the server locally:
npm run mcp
Option 2: Install from npm
npm install -g @n0zer0d4y/mercury-spec-ops

Or use directly with npx (no installation required):

npx @n0zer0d4y/mercury-spec-ops

Usage

Integration with MCP Clients

Configure the server in your MCP client (Claude Desktop, Cursor, etc.):

Using npx (Recommended - No Installation)

For Claude Desktop (claude_desktop_config.json):

{

"mcpServers": { "mercury-spec-ops": { "command": "npx", "args": ["-y", "@n0zer0d4y/mercury-spec-ops"] } } }

For Cursor (~/.cursor/mcp.json):

{

"mcpServers": { "mercury-spec-ops": { "timeout": 60, "type": "stdio", "command": "npx", "args": ["-y", "@n0zer0d4y/mercury-spec-ops"] } } }

Using Local Installation

For Claude Desktop (claude_desktop_config.json):

{

"mcpServers": { "mercury-spec-ops": { "command": "node", "args": ["/path/to/mercury-spec-ops/dist/src/server.js"] } } }

For Cursor (~/.cursor/mcp.json):

{

"mcpServers": { "mercury-spec-ops": { "timeout": 60, "type": "stdio", "command": "node", "args": ["/path/to/mercury-spec-ops/dist/src/server.js"] } } }

Windows Local Path Example (Cursor):

{

"mcpServers": { "mercury-spec-ops": { "timeout": 60, "type": "stdio", "command": "node", "args": [ "C:\\Development\\Projects\\MCP-Servers\\mercury-spec-ops\\dist\\src\\server.js" ] } } }

Important Notes:

  • npx: Automatically fetches the latest version from npm (recommended for most users)
  • Local: Replace the path with your actual project location and run npm run build first
  • Restart: Restart your MCP client after configuration changes

Workflow Examples

Example 1: Enhanced Codebase Analysis
  1. User Prompt: "Analyze this Node.js/TypeScript codebase focusing on security and performance"
  2. Claude Desktop Action:
  • Calls the codebase-analysis-prompt with technology_stack: "node.js,typescript" and analysis_focus: "security,performance"
  • System programmatically assembles: base prompt + Node.js module + TypeScript module + security module + performance module
  • The prompt instructs Claude to first read the resource://codebase-analysis-template
  • Template builder assembles: base template + Node.js sections + TypeScript sections + security sections + performance sections
  • Claude reads the dynamically assembled template
  • Claude analyzes the codebase and generates a comprehensive technology-specific report
Example 2: Enhanced Bug Analysis
  1. User Prompt: "Analyze this critical security bug in React application"
  2. Claude Desktop Action:
  • Calls the bug-analysis-prompt with technology_stack: "react", severity_level: "critical", and bug_type: "security"
  • System assembles: base prompt + React module + security module + critical severity module
  • Template includes: general sections + React-specific + security-specific + critical-severity sections
  • Claude reads the bug analysis template
  • Claude analyzes the bug and generates a comprehensive technology and severity-specific report
Example 3: PRD Generation with Technology Support
  1. User Prompt: "Help me create a PRD for a React-based task management application"
  2. Claude Desktop Action:
  • Calls the prd-prompt with project details and technology_stack: "react"
  • System assembles: base prompt + React module
  • Template builder includes: base PRD template + React-specific considerations
  • Claude reads the technology-enhanced PRD template
  • Claude generates a comprehensive PRD with React-specific sections

Prompt Details

PRD Prompt
  • Name: prd-prompt
  • Arguments:
  • project_name (required): Name of the software project
  • project_description (required): Brief description of the project
  • target_audience (optional): Target audience for the product (enum values)
  • project_type (optional): Type of project (enum values)
  • key_features (optional): Key features to include in the PRD
  • technology_stack (optional): Technology stack to be used (enum values)
  • custom_instructions (optional): Custom instructions for PRD generation
Codebase Analysis Prompt
  • Name: codebase-analysis-prompt
  • Arguments:
  • repository_path (required): Path to the repository to analyze
  • technology_stack (required): Technology stack used in the codebase (enum values)
  • analysis_focus (optional): Focus areas for analysis (enum values)
  • custom_instructions (optional): Custom instructions for codebase analysis
Bug Analysis Prompt
  • Name: bug-analysis-prompt
  • Arguments:
  • repository_path (required): Path to the repository with bugs to analyze
  • bug_description (required): Description of the bug or issue to analyze
  • affected_components (optional): Components affected by the bug
  • severity_level (optional): Severity level of the bug (enum values)
  • bug_type (optional): Type of bug (enum values)
  • technology_stack (optional): Technology stack used in the affected components (enum values)
  • custom_instructions (optional): Custom instructions for bug analysis

Resource Details

PRD Template
  • URI: resource://prd-template
  • Format: Markdown
  • Content: Modular PRD template with technology-specific sections that dynamically assemble based on input parameters
Codebase Analysis Template
  • URI: resource://codebase-analysis-template
  • Format: Markdown
  • Content: Modular codebase analysis template with technology and focus-specific sections that assemble based on analysis parameters
Bug Analysis Template
  • URI: resource://bug-analysis-template
  • Format: Markdown
  • Content: Modular bug analysis template with severity and technology-specific sections that assemble based on bug parameters

Development

Project Structure
src/

├── server.ts # Main MCP server implementation ├── types/ │ ├── enums.ts # All enum definitions (31 tech + 10 focus) │ └── index.ts # Type definitions and exports ├── prompts/ │ ├── modules/ # 45 modular prompt components │ │ ├── technology/ # 31 technology-specific modules │ │ │ ├── languages/ # 11 language modules │ │ │ ├── runtimes/ # 1 runtime module │ │ │ ├── frameworks/ # 10 framework modules (3 frontend, 7 backend) │ │ │ ├── databases/ # 4 database modules │ │ │ ├── cloud/ # 3 cloud platform modules │ │ │ └── tools/ # 2 DevOps tool modules │ │ ├── analysis-focus/ # 10 focus-area modules │ │ └── bug-severity/ # 4 severity-specific modules │ ├── base-prompts/ # Base prompt templates │ └── prompt-builder.ts # Programmatic prompt assembly ├── resources/ │ ├── templates/ # Modular template components │ │ ├── prd/ # 10 PRD template modules │ │ ├── codebase-analysis/ # 8 codebase analysis modules │ │ └── bug-analysis/ # 4 bug analysis modules │ └── template-builder.ts # Programmatic template assembly └── tests/ # Comprehensive test suite (66 tests) ├── utils/ # Utility function tests (enum parser) ├── prompts/ # Prompt builder tests ├── resources/ # Template builder tests └── types/ # Enum and type tests

Testing

The project includes a comprehensive test suite with 88% coverage:

# Run all tests

npm test

Run tests in watch mode

npm run test:watch

Run tests with coverage report

npm run test:coverage

Run tests with interactive UI

npm run test:ui

Test MCP integration

npm run t

只显示了开头部分;其余内容请在仓库中阅读

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