What can you do with this server?

The MCP Prompts Server is a tool for managing and applying AI prompts and templates with robust storage and orchestration capabilities. With this server, you can: * Store and retrieve prompts: Add new prompts with metadata and retrieve them by ID * Create and manage templates: Define templates with variables for consistent prompting * Apply variables to templates: Dynamically fill templates with specific values * Filter and sort prompts: List prompts by tags, category, or search terms with pagination * Update and delete prompts: Modify or remove existing prompts * Access multiple storage backends: Use file storage, PostgreSQL, or MDC format * Integrate with AI tools: Seamlessly work with platforms like Claude * Project orchestration: Manage complex AI systems and workflows * Support multi-format prompts: Work with JSON, MDC, PGAI, and Template formats * Health check endpoints: Monitor system status

Which integrations are available for this server?

Supports deployment using Docker and Docker Compose for containerized deployment Built on Node.js with support for Node.js 18 or later Provides integration with PostgreSQL databases for storing prompts, with export/import functionality and synchronization between file storage and database Built using TypeScript, as indicated by the project structure and build process

How do I use MCP Prompts Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Prompts Server create a code review prompt for Python with variables for language and code" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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MCP Prompts Server

by sparesparrow

Overview Schema Source Related Servers Discussions Admin

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:

Detects project type
Loads appropriate main agent
Spawns specialized subagents
Runs comprehensive analysis
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 deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityStale

ResponsivenessUnresponsive

Resources

Need Help?

Reddit Discussion

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