Mercury Spec Ops

Mercury Spec Ops MCP Server: Prompts and Resources as Tools

[TypeScript] [MCP Registry] [MCP Dev] [MCP Server] [MCP Server with Tools] [standard-readme compliant] [License: MIT] [MseeP.ai Security Assessment Badge]

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.gitcd mercury-spec-opsnpm installnpm run build

2. 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 testsnpm test
# Run tests in watch modenpm run test:watch
# Run tests with coverage reportnpm run test:coverage
# Run tests with interactive UInpm run test:ui
# Test MCP integrationnpm run test:mcp

Test Statistics:

• 66 tests (100% passing)
• 88.48% overall coverage
• Statement coverage: 88.48%
• Line coverage: 88.70%

Building for Production

npm run build

Linting

npm run lint

Extending the System

Adding New Technology Modules

1. Add the technology to the TechnologyStack enum in src/types/enums.ts
2. Create a new module file in the appropriate category:
   • Languages: src/prompts/modules/technology/languages/
   • Frameworks: src/prompts/modules/technology/frameworks/frontend/ or backend/
   • Databases: src/prompts/modules/technology/databases/
   • Cloud: src/prompts/modules/technology/cloud/
   • Tools: src/prompts/modules/technology/tools/
3. Implement the TechnologyPromptModule interface
4. Export from the category's index.ts
5. Register it in prompt-builder.ts
6. Add corresponding template sections in src/resources/templates/
7. Write tests in src/__tests__/

Adding New Focus Areas

1. Add the focus area to the AnalysisFocus enum in src/types/enums.ts
2. Create a new module file in src/prompts/modules/analysis-focus/
3. Implement the AnalysisFocusPromptModule interface
4. Export from analysis-focus/index.ts
5. Register it in prompt-builder.ts
6. Add corresponding template sections
7. Write tests

Adding New Severity Levels

1. Add the severity to the BugSeverity enum in src/types/enums.ts
2. Create a new module file in src/prompts/modules/bug-severity/
3. Implement the BugSeverityPromptModule interface
4. Register it in the prompt builder
5. Add corresponding template sections
6. Write tests

Contributing

Contributions are welcome! Please follow these guidelines:

• Fork the repository and create a feature branch
• Follow existing code patterns and maintain test coverage above 85%
• Use Conventional Commits format (feat, fix, docs, test, chore)
• Ensure all tests pass before submitting a pull request
• See Extending the System for adding new modules

Report issues on GitHub Issues with clear reproduction steps.

License

This project is licensed under the MIT License - see LICENSE file for details.