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 "Install 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.

MCP Prompts Server

NPM Version License TypeScript MCP

A robust, extensible MCP (Model Context Protocol) server for managing, versioning, and serving prompts and templates for LLM applications with AWS integration.

Features • Installation • Quick Start • Configuration • API • Tools • Docker

Overview

MCP Prompts is a production-ready server that implements the Model Context Protocol (MCP) to provide intelligent prompt management, template systems, and AI-powered workflows. It supports multiple storage backends including in-memory, file-based, and AWS services (DynamoDB, S3, SQS).

Key Capabilities

Prompt Management: Create, read, update, delete, and version prompts
Template System: Variable substitution with type validation
Search & Discovery: Tag-based filtering and full-text search
Access Control: Role-based access with subscription tiers
AWS Integration: Native DynamoDB, S3, and SQS support
Rate Limiting: Configurable per-user and per-tier limits
Subscription Management: Stripe integration for payments
Multi-Mode: Run as MCP server (stdio) or HTTP REST API
Docker Support: Multiple deployment configurations

Related MCP server: PromptLab MCP Server

Cognitive Architecture 🧠

MCP Prompts implements a seven-layer cognitive architecture that transforms the system into an intelligent development assistant capable of learning from experience and adapting to different domains.

Seven Cognitive Layers

┌─────────────────────────────────────────┐
│ 7. Evaluative    │ Quality Assessment   │
│                  │ Priority Judgment    │
├─────────────────────────────────────────┤
│ 6. Transfer      │ Cross-Domain Analogies│
│                  │ Pattern Abstraction  │
├─────────────────────────────────────────┤
│ 5. Meta-Cognitive│ Strategy Selection   │
│                  │ Self-Awareness       │
├─────────────────────────────────────────┤
│ 4. Procedural    │ Workflows & Techniques│
│                  │ Analysis Procedures  │
├─────────────────────────────────────────┤
│ 3. Semantic      │ Domain Knowledge     │
│                  │ Tool Capabilities    │
├─────────────────────────────────────────┤
│ 2. Episodic      │ Problem-Solving      │
│                  │ Experience Memory    │
├─────────────────────────────────────────┤
│ 1. Perceptual    │ Context Detection    │
│                  │ Goal Identification  │
└─────────────────────────────────────────┘

Intelligent Prompt Management

Context-Aware: Automatically detects project types and applies relevant knowledge
Experience Learning: Captures successful problem-solving patterns for reuse
Cross-Domain Transfer: Applies patterns learned in one domain to others
Self-Improving: Learns from usage patterns to improve recommendations

FlatBuffers Integration

High-performance binary serialization for cognitive data:

Zero-copy deserialization for maximum speed
Schema evolution supporting backward compatibility
Inter-server communication with minimal overhead
Embedded optimization for resource-constrained environments

Features

Core Features

✅ MCP Protocol Support: Full implementation of MCP 1.18 specification
🔧 Multiple Storage Backends: Memory, File System, AWS (DynamoDB/S3)
📝 Prompt Templates: Advanced variable substitution and validation
🔍 Advanced Search: Category, tag, and content-based search
🔒 Security: Helmet, CORS, rate limiting, and authentication
📊 Monitoring: CloudWatch metrics and structured logging
💳 Payment Processing: Stripe integration with webhook support
🌐 REST API: Optional HTTP server mode for web integrations
🐳 Docker Ready: Multiple Dockerfile variants for different use cases

MCP Tools

The server exposes the following MCP tools:

Prompt Management Tools

add_prompt - Create a new prompt with metadata
get_prompt - Retrieve a prompt by ID
list_prompts - List all prompts with optional filtering
update_prompt - Update an existing prompt
delete_prompt - Delete a prompt
apply_template - Apply variables to a prompt template
get_stats - Get statistics about stored prompts

Template System

Templates support variable substitution with the {{variableName}} syntax:

Please review this {{language}} code for:
- Security issues
- Performance improvements
- Best practices

Code:
{{code}}

Installation

NPM Package

npm install @sparesparrow/mcp-prompts
# or
pnpm add @sparesparrow/mcp-prompts
# or
yarn add @sparesparrow/mcp-prompts

Global CLI

npm install -g @sparesparrow/mcp-prompts
mcp-prompts --help

Docker

docker pull ghcr.io/sparesparrow/mcp-prompts:latest

Quick Start

As MCP Server (stdio)

Add to your MCP client configuration (e.g., Claude Desktop):

{
  "mcpServers": {
    "mcp-prompts": {
      "command": "npx",
      "args": ["-y", "@sparesparrow/mcp-prompts"]
    }
  }
}

Or using Docker:

{
  "mcpServers": {
    "mcp-prompts": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-v",
        "${HOME}/.mcp-prompts:/app/data",
        "ghcr.io/sparesparrow/mcp-prompts:mcp"
      ]
    }
  }
}

As HTTP Server

# Using npm
npm install @sparesparrow/mcp-prompts
MODE=http PORT=3000 node node_modules/@sparesparrow/mcp-prompts/dist/index.js

# Using Docker
docker run -p 3000:3000 -e MODE=http ghcr.io/sparesparrow/mcp-prompts:latest

Using CLI

# Start in MCP mode
mcp-prompts start --mode mcp

# Start HTTP server
mcp-prompts start --mode http --port 3000

# List prompts
mcp-prompts list

# Get a prompt
mcp-prompts get <prompt-id>

# Create a prompt
mcp-prompts create \
  --name "Code Review" \
  --template "Review this {{language}} code..." \
  --category development \
  --tags "code-review,development"

# Search prompts
mcp-prompts search "bug fix"

# Check health
mcp-prompts health

Configuration

Environment Variables

Core Settings

# Server mode: 'mcp' for stdio or 'http' for REST API
MODE=mcp

# HTTP server settings (when MODE=http)
PORT=3000
HOST=0.0.0.0
NODE_ENV=production

# Storage backend: 'memory', 'file', or 'aws'
STORAGE_TYPE=memory

# Logging
LOG_LEVEL=info

AWS Configuration (when using AWS storage)

AWS_REGION=us-east-1
PROMPTS_TABLE=mcp-prompts
PROMPTS_BUCKET=mcp-prompts-catalog
PROCESSING_QUEUE=mcp-prompts-processing
USERS_TABLE=mcp-prompts-users

# AWS credentials (use IAM roles in production)
AWS_ACCESS_KEY_ID=your-access-key
AWS_SECRET_ACCESS_KEY=your-secret-key

Payment Integration (Optional)

STRIPE_SECRET_KEY=sk_test_...
STRIPE_WEBHOOK_SECRET=whsec_...
STRIPE_PUBLISHABLE_KEY=pk_test_...

Storage Backends

Memory Storage (Default)

Best for development and testing:

STORAGE_TYPE=memory

AWS Storage

Production-ready with DynamoDB and S3:

STORAGE_TYPE=aws
AWS_REGION=us-east-1
PROMPTS_TABLE=mcp-prompts
PROMPTS_BUCKET=mcp-prompts-catalog

File Storage

Persistent local storage:

STORAGE_TYPE=file
DATA_DIR=/path/to/data

API

HTTP Endpoints (when MODE=http)

Health & Status

GET  /health                      - Health check
GET  /mcp                          - MCP capabilities
GET  /mcp/tools                    - List available MCP tools
POST /mcp/tools                    - Execute an MCP tool

Prompts API

GET    /v1/prompts                 - List prompts
GET    /v1/prompts/:id             - Get specific prompt
POST   /v1/prompts                 - Create new prompt
PUT    /v1/prompts/:id             - Update prompt
DELETE /v1/prompts/:id             - Delete prompt
POST   /v1/prompts/:id/apply       - Apply template variables

Slash Commands

GET  /v1/slash-commands            - List available slash commands
GET  /v1/slash-commands/suggest    - Get command suggestions
POST /v1/slash-commands/execute    - Execute a slash command

Subscriptions & Payments

GET  /v1/subscription/plans        - Get subscription plans
GET  /v1/subscription/status       - Get user subscription status
POST /v1/payment/create-intent     - Create payment intent
POST /v1/subscription/create       - Create subscription
POST /v1/subscription/cancel       - Cancel subscription
POST /v1/webhook/stripe            - Stripe webhook handler

Example API Usage

Create a Prompt

curl -X POST http://localhost:3000/v1/prompts \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Bug Analyzer",
    "content": "Analyze this bug: {{description}}",
    "isTemplate": true,
    "tags": ["debugging", "analysis"],
    "variables": [
      {
        "name": "description",
        "description": "Bug description",
        "required": true,
        "type": "string"
      }
    ],
    "metadata": {
      "category": "debugging"
    }
  }'

List Prompts

# List all prompts
curl http://localhost:3000/v1/prompts

# Filter by category
curl http://localhost:3000/v1/prompts?category=development&limit=10

# Search
curl http://localhost:3000/v1/prompts?search=code%20review

Apply Template

curl -X POST http://localhost:3000/v1/prompts/bug_analyzer/apply \
  -H "Content-Type: application/json" \
  -d '{
    "variables": {
      "description": "Login page crashes on mobile devices"
    }
  }'

Available Tools

MCP Tools Reference

When connected to an MCP client, the following tools are available:

`add_prompt`

Create a new prompt.

Parameters:

name (string, required): Prompt name
content (string, required): Prompt content/template
isTemplate (boolean): Whether this is a template
tags (array): Tags for categorization
variables (array): Template variables definition
metadata (object): Additional metadata

`get_prompt`

Retrieve a specific prompt by ID.

Parameters:

id (string, required): Prompt ID

`list_prompts`

List all prompts with optional filtering.

Parameters:

tags (array, optional): Filter by tags
search (string, optional): Search term

`update_prompt`

Update an existing prompt.

Parameters:

id (string, required): Prompt ID
updates (object, required): Fields to update

`delete_prompt`

Delete a prompt.

Parameters:

id (string, required): Prompt ID

`apply_template`

Apply variables to a prompt template.

Parameters:

id (string, required): Template ID
variables (object, required): Variable values

`get_stats`

Get statistics about stored prompts.

Returns:

Total prompts count
Templates count
Regular prompts count
Available tags
Available categories

Docker

Available Images

# Default image (HTTP mode)
ghcr.io/sparesparrow/mcp-prompts:latest

# MCP server mode (stdio)
ghcr.io/sparesparrow/mcp-prompts:mcp

# AWS integration
ghcr.io/sparesparrow/mcp-prompts:aws

# Memory storage
ghcr.io/sparesparrow/mcp-prompts:memory

# File storage
ghcr.io/sparesparrow/mcp-prompts:file

Docker Compose

version: '3.8'

services:
  mcp-prompts:
    image: ghcr.io/sparesparrow/mcp-prompts:latest
    ports:
      - "3000:3000"
    environment:
      - MODE=http
      - PORT=3000
      - STORAGE_TYPE=memory
      - LOG_LEVEL=info
    volumes:
      - ./data:/app/data
    restart: unless-stopped

Build from Source

# Build default image
docker build -t mcp-prompts:latest .

# Build MCP server variant
docker build -f Dockerfile.mcp -t mcp-prompts:mcp .

# Build AWS variant
docker build -f Dockerfile.aws -t mcp-prompts:aws .

Development

Prerequisites

Node.js 18+ or compatible runtime
pnpm 8+ (or npm/yarn)
Docker (optional)
AWS CLI (for AWS deployments)

Setup

# Clone repository
git clone https://github.com/sparesparrow/mcp-prompts.git
cd mcp-prompts

# Install dependencies
pnpm install

# Build
pnpm run build

# Run tests
pnpm test

# Run in development mode
pnpm run dev

# Run HTTP server
pnpm run dev:http

# Run MCP server
pnpm run dev:mcp

Project Structure

mcp-prompts/
├── src/
│   ├── adapters/          # Storage adapters (AWS, Memory, File)
│   ├── core/              # Core domain logic
│   │   ├── entities/      # Domain entities
│   │   ├── services/      # Business logic services
│   │   └── ports/         # Interfaces
│   ├── mcp/               # MCP server implementation
│   ├── lambda/            # AWS Lambda handlers
│   ├── monitoring/        # CloudWatch metrics
│   ├── cli.ts             # CLI entry point
│   ├── index.ts           # HTTP server entry point
│   └── mcp-server-standalone.ts  # MCP stdio server
├── data/                  # Sample data
├── cdk/                   # AWS CDK infrastructure
├── scripts/               # Utility scripts
├── Dockerfile.*           # Docker configurations
└── package.json

AWS Deployment

Using AWS CDK

# Configure AWS credentials
aws configure

# Install dependencies
pnpm install

# Deploy infrastructure
cd cdk
cdk deploy --all

# Or use npm script
pnpm run cdk:deploy

Manual Deployment

# Deploy using script
./scripts/deploy-aws.sh

# Cleanup resources
./scripts/cleanup-aws.sh

Required AWS Resources

DynamoDB table for prompts storage
S3 bucket for catalog and artifacts
SQS queue for async processing
Lambda functions for serverless execution
API Gateway for HTTP endpoints
CloudWatch for monitoring
Cognito for authentication (optional)

Sample Prompts

The server includes several sample prompts:

Code Review Assistant: Comprehensive code review template
Documentation Writer: Technical documentation generator
Bug Analyzer: Bug report analysis and investigation
Architecture Reviewer: System architecture evaluation
Test Case Generator: Automated test case creation

Monitoring & Observability

Logging

Structured JSON logging with pino:

import pino from 'pino';

const logger = pino({
  level: process.env.LOG_LEVEL || 'info'
});

Metrics (AWS)

CloudWatch metrics for:

Request rates
Error rates
Latency
Prompt usage
Template applications

Health Checks

# HTTP health check
curl http://localhost:3000/health

# CLI health check
mcp-prompts health

Security

Best Practices

✅ Runs as non-root user in Docker
✅ Helmet middleware for HTTP security headers
✅ CORS configuration
✅ Rate limiting per user/tier
✅ Input validation with Zod
✅ AWS IAM roles for production
✅ Secrets management via environment variables
✅ Regular dependency updates

Authentication

The HTTP server supports authentication via:

Bearer tokens in Authorization header
API Gateway Cognito authorizer (AWS)
Custom authentication middleware

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

How to Contribute

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Troubleshooting

Common Issues

MCP server not starting

Check that no other process is using stdio
Verify Node.js version (18+ required)
Check logs: LOG_LEVEL=debug mcp-prompts start

HTTP server connection refused

Verify port is not in use: lsof -i :3000
Check firewall settings
Ensure MODE=http is set

AWS connection failures

Verify AWS credentials: aws sts get-caller-identity
Check IAM permissions for DynamoDB, S3, SQS
Confirm region is correct

Template variables not substituting

Ensure template has isTemplate: true
Verify variable names match (case-sensitive)
Check variable syntax: {{variableName}}

License

MIT License - see LICENSE file for details.

Support

📖 Documentation
🐛 Issue Tracker
💬 Discussions
📧 Email: support@sparesparrow.com

Resources

Acknowledgments

Built with:

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Related Servers

MCP Prompts Server

Overview

Key Capabilities

Cognitive Architecture 🧠

Seven Cognitive Layers

Intelligent Prompt Management

FlatBuffers Integration

Features

Core Features

MCP Tools

Prompt Management Tools

Template System

Installation

NPM Package

Global CLI

Docker

Quick Start

As MCP Server (stdio)

As HTTP Server

Using CLI

Configuration

Environment Variables

Core Settings

AWS Configuration (when using AWS storage)

Payment Integration (Optional)

Storage Backends

Memory Storage (Default)

AWS Storage

File Storage

API

HTTP Endpoints (when MODE=http)

Health & Status

Prompts API

Slash Commands

Subscriptions & Payments

Example API Usage

Create a Prompt

List Prompts

Apply Template

Available Tools

MCP Tools Reference

add_prompt

get_prompt

list_prompts

update_prompt

delete_prompt

apply_template

get_stats

Docker

Available Images

Docker Compose

Build from Source

Development

Prerequisites

Setup

Project Structure

AWS Deployment

Using AWS CDK

Manual Deployment

Required AWS Resources

Sample Prompts

Monitoring & Observability

Logging

Metrics (AWS)

Health Checks

Security

Best Practices

Authentication

Contributing

How to Contribute

Troubleshooting

Common Issues

License

Support

Resources

Acknowledgments

Resources

Appeared in Searches

Latest Blog Posts

MCP directory API

`add_prompt`

`get_prompt`

`list_prompts`

`update_prompt`

`delete_prompt`

`apply_template`

`get_stats`