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Mercury Spec Ops

by n0zer0d4yversion 0.1.2i★ –iRuns on your computer
Registry nameio.github.n0zer0d4y/mercury-spec-ops

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • npmNode.js installed (for the npx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

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

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account n0zer0d4y, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 5671 servers in this category
  • Published to the registry more recently than 1% of the other 5671 servers in this category
  • Basic checks equal to or better than 100% of the other 5671 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Mercury Spec Ops safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.2) reached the registry 12 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday
Install

Codebase Memory

by DeusData

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

Runs on your computer★ 48,148MITUpdated 21 days ago
Install

Exa

by exa-labs

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

Runs in the cloud★ 5,213MITUpdated 2 months ago
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Only the beginning is shown; read the rest in the repo

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