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sparesparrow

MCP Prompts Server

by sparesparrow
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TypeScript
Hybrid

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

Related MCP server: Teleprompter

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

This server cannot be installed

A
license - permissive license
-
quality - not tested
C
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
1Releases (12mo)
Commit activity
Issues opened vs closed

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