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sparesparrow/mcp-prompts

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Simple MCP server for managing AI prompts and agent configurations with direct claude CLI orchestration.

Overview

Simple MCP server for managing AI prompts and agent configurations with direct claude CLI orchestration.

README

mcp-prompts

Simple MCP server for managing AI prompts and agent configurations with direct claude CLI orchestration.

What It Does

  • Stores prompts as JSON files in data/prompts/
  • Exposes MCP tools for querying and managing prompts
  • Provides agent templates for project orchestration
  • Works with Claude Desktop, Cursor, and other MCP clients

Quick Start

1. Install

pnpm install
pnpm build

2. Start MCP Server

pnpm start

3. Configure Claude Desktop

Add to ~/.claude/mcp.json:

{
  "mcpServers": {
    "prompts": {
      "command": "node",
      "args": ["/absolute/path/to/mcp-prompts/dist/mcp-server-standalone.js"],
      "env": {
        "PROMPTS_DIR": "/absolute/path/to/mcp-prompts/data/prompts"
      }
    }
  }
}

4. Use in Claude

Ask Claude:

  • “List all prompts tagged with esp32”
  • “Get the esp32-fft-configuration-guide prompt”
  • “Create a new prompt for Python FastAPI best practices”

Orchestrating Projects

Use the orchestrate script to analyze entire projects:

./scripts/orchestrate-project.sh ~/projects/mia analyze
./scripts/orchestrate-project.sh ~/projects/esp32-bpm-detector review

This automatically:

  1. Detects project type
  2. Loads appropriate main agent
  3. Spawns specialized subagents
  4. Runs comprehensive analysis
  5. Returns structured results

MCP Tools

Tool Description
list_prompts Query prompts with filters (tags, search, category)
get_prompt Retrieve specific prompt with template expansion
create_prompt Add new prompt to repository
update_prompt Modify existing prompt
delete_prompt Remove prompt
apply_template Apply variables to template string
get_stats Repository statistics

Prompts Organization

data/prompts/
├── main-agents/           # 7 project orchestration templates
│   ├── main_agent_python_backend.json
│   ├── main_agent_cpp_backend.json
│   ├── main_agent_android_app.json
│   ├── main_agent_embedded_iot.json
│   ├── main_agent_multiplatform_iot.json
│   └── ...
│
├── subagents/            # 19 specialized analysis agents
│   ├── explorer.json     # Project discovery
│   ├── analyzer.json     # Code analysis
│   ├── diagrammer.json   # Diagram generation
│   ├── solid_analyzer.json # Code quality
│   └── ...
│
├── cognitive/            # 7-layer cognitive architecture
├── esp32/                # Embedded systems patterns
├── mcp-tools/           # MCP usage patterns
└── [domains]/           # Domain-specific knowledge

Architecture

Simple and focused:

┌──────────────────────────────┐
│   MCP Server (stdio)         │
│   ├── list_prompts           │
│   ├── get_prompt             │
│   ├── create_prompt          │
│   └── ...                    │
├──────────────────────────────┤
│   File Storage               │
│   └── data/prompts/*.json    │
└──────────────────────────────┘

Orchestration:
  orchestrate-project.sh
    ↓ loads prompts
    ↓ builds agent config
    ↓ calls claude CLI
  Actual agent execution

Development

pnpm install       # Install dependencies
pnpm build         # Build TypeScript
pnpm dev           # Watch mode
pnpm test          # Run tests
pnpm orchestrate   # Test orchestration

Enterprise Deployment

For enterprise features (AWS, multi-tenant, payments), see archive/aws/README.md.

Most users don’t need this - the local MCP server is sufficient.

License

MIT

View this README on GitHub

Install

This server does not publish a one-line install command.

Open the repository installation guide

Configuration

{ "mcpServers": { "prompts": { "command": "node", "args": ["/absolute/path/to/mcp-prompts/dist/mcp-server-standalone.js"], "env": { "PROMPTS_DIR": "/absolute/path/to/mcp-prompts/data/prompts" } } } }