mcp-prompts

Model Context Protocol server for managing, storing, and providing prompts and prompt templates for LLM interactions.

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mcp-prompts is a Python library that provides a collection of prompts for generating creative writing ideas. It includes a variety of prompts such as story starters, character development, plot twists, and more. The library is designed to inspire writers and help them overcome writer's block by offering unique and engaging prompts to spark creativity. With mcp-prompts, users can access a wide range of writing prompts to kickstart their imagination and enhance their storytelling skills.

README:

MCP-Prompts

A robust, extensible server for managing, versioning, and serving prompts and templates for LLM applications, built on the Model Context Protocol (MCP) with multi-backend support including AWS, PostgreSQL, and file storage.

✨ Features

🔄 Multiple Storage Backends: DynamoDB, PostgreSQL, and local file storage
📦 Model Context Protocol: Full MCP compliance for seamless AI integration
🏗️ Hexagonal Architecture: Clean separation of concerns with adapter pattern
⚡ Serverless Ready: AWS Lambda deployment with API Gateway
🔍 Advanced Querying: Filter by category, tags, and metadata
📊 Version Control: Complete prompt versioning and history
🔐 Enterprise Security: IAM, VPC, and encryption support
📈 Monitoring: CloudWatch metrics and comprehensive logging
🧪 Testing: Full test suite with local development support

🚀 Quick Start

Prerequisites

Node.js 18+ and PNPM
AWS CLI (for AWS deployments)
PostgreSQL (for database deployments)
Docker (for containerized deployments)

Choose Your Deployment

🏠 Local Development (File Storage)

# Install dependencies
pnpm install

# Start with memory storage (fastest)
STORAGE_TYPE=memory pnpm run dev

# Or use file storage
STORAGE_TYPE=file PROMPTS_DIR=./data/prompts pnpm run dev

🐘 PostgreSQL Database

# Deploy with PostgreSQL
./scripts/deploy-postgres.sh

# Or run locally
STORAGE_TYPE=postgres pnpm run dev

☁️ AWS Production (Recommended)

# One-command AWS deployment
./scripts/deploy-aws-enhanced.sh

🏗️ Architecture

This project follows hexagonal architecture (ports & adapters) with clean separation between:

Core Business Logic: Prompt management, versioning, and validation
Ports: Interfaces for storage, events, and external services
Adapters: Concrete implementations (DynamoDB, PostgreSQL, S3, etc.)
Infrastructure: AWS services, HTTP servers, and CLI tools

Component Diagram

┌─────────────────────────────────────┐
│           Presentation Layer        │
│  ┌─────────────┐ ┌────────────────┐ │
│  │   CLI Tool  │ │  HTTP Server   │ │
│  └─────────────┘ └────────────────┘ │
└─────────────────────────────────────┘
                 │
┌─────────────────────────────────────┐
│         Application Layer           │
│  ┌────────────────────────────────┐ │
│  │  Prompt Service & Use Cases   │ │
│  └────────────────────────────────┘ │
└─────────────────────────────────────┘
                 │
┌─────────────────────────────────────┐
│            Domain Layer             │
│  ┌────────────────────────────────┐ │
│  │  Prompt Entities & Business   │ │
│  │        Logic                  │ │
│  └────────────────────────────────┘ │
└─────────────────────────────────────┘
                 │
┌─────────────────────────────────────┐
│         Infrastructure Layer        │
│  ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐    │
│  │DDB  │ │PG   │ │S3   │ │SQS  │    │
│  │Adapter│ │Adapter│ │Adapter│ │Adapter│   │
│  └─────┘ └─────┘ └─────┘ └─────┘    │
└─────────────────────────────────────┘

🔄 Storage Types

1. 📁 File Storage (Development)

Best for: Development, testing, offline usage

Features:

Simple JSON file-based storage
No external dependencies
Easy backup and migration
Perfect for local development

Configuration:

STORAGE_TYPE=file
PROMPTS_DIR=./data/prompts

Usage:

STORAGE_TYPE=file pnpm run dev

2. 🐘 PostgreSQL Database

Best for: Production applications, complex queries, ACID compliance

Features:

Full ACID compliance
Complex queries and indexing
Session management
Backup and restore capabilities
Concurrent access support

Configuration:

STORAGE_TYPE=postgres
DB_HOST=localhost
DB_PORT=5432
DB_NAME=mcp_prompts
DB_USER=mcp_user
DB_PASSWORD=secure_password

Deployment:

./scripts/deploy-postgres.sh

3. ☁️ AWS Services (Production)

Best for: Scalable production, cloud-native deployments

Features:

DynamoDB for metadata storage
S3 for catalog and assets
SQS for event processing
API Gateway for HTTP endpoints
CloudFront for global CDN
CloudWatch for monitoring

Configuration:

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

Deployment:

./scripts/deploy-aws-enhanced.sh

4. 💾 Memory Storage (Testing)

Best for: Unit testing, CI/CD pipelines, temporary deployments

Features:

In-memory storage
No persistence
Fastest performance
Sample data included

Configuration:

STORAGE_TYPE=memory

📦 Installation

NPM Package

# Install globally
npm install -g @sparesparrow/mcp-prompts

# Or use with npx
npx @sparesparrow/mcp-prompts --help

From Source

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

# Install dependencies
pnpm install

# Build project
pnpm run build

# Run locally
STORAGE_TYPE=memory pnpm run dev

Docker

# Build image
docker build -t mcp-prompts .

# Run with different storage types
docker run -p 3000:3000 -e STORAGE_TYPE=memory mcp-prompts
docker run -p 3000:3000 -e STORAGE_TYPE=file -v $(pwd)/data:/app/data mcp-prompts

⚙️ Configuration

Environment Variables

Core Configuration

Variable	Description	Default	Required
`STORAGE_TYPE`	Storage backend (memory/file/postgres/aws)	`memory`	Yes
`NODE_ENV`	Environment (development/production)	`development`	No
`LOG_LEVEL`	Logging level (debug/info/warn/error)	`info`	No
`PORT`	HTTP server port	`3000`	No

File Storage Configuration

Variable	Description	Default	Required
`PROMPTS_DIR`	Directory for prompt files	`./data/prompts`	No

PostgreSQL Configuration

Variable	Description	Default	Required
`DB_HOST`	Database host	`localhost`	Yes
`DB_PORT`	Database port	`5432`	No
`DB_NAME`	Database name	`mcp_prompts`	No
`DB_USER`	Database user	-	Yes
`DB_PASSWORD`	Database password	-	Yes
`DB_SSL`	Enable SSL connection	`false`	No

AWS Configuration

Variable	Description	Default	Required
`AWS_REGION`	AWS region	`us-east-1`	Yes (for AWS storage)
`AWS_PROFILE`	AWS profile name	`default`	No
`PROMPTS_TABLE`	DynamoDB table name	`mcp-prompts`	No
`SESSIONS_TABLE`	Sessions table name	`mcp-sessions`	No
`PROMPTS_BUCKET`	S3 bucket name	`mcp-prompts-catalog-{account}-{region}`	No
`PROCESSING_QUEUE`	SQS queue URL	-	No
`CATALOG_SYNC_QUEUE`	Catalog sync queue URL	-	No

🔌 API Reference

REST API Endpoints

Health & Status

GET /health - Service health check with component status
GET /mcp - MCP server capabilities and supported features

Prompts Management

GET /v1/prompts - List prompts with optional filtering
- Query parameters: ?category=, ?limit=, ?offset=, ?tags=
POST /v1/prompts - Create new prompt
GET /v1/prompts/{id} - Get specific prompt by ID
PUT /v1/prompts/{id} - Update existing prompt
DELETE /v1/prompts/{id} - Delete prompt
POST /v1/prompts/{id}/apply - Apply template variables

MCP Tools

GET /mcp/tools - List available MCP tools
POST /mcp/tools - Execute MCP tool with parameters

Request/Response Examples

Create Prompt

curl -X POST http://localhost:3000/v1/prompts \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Code Review Assistant",
    "description": "Advanced code review helper",
    "template": "Please review this {{language}} code:\n\n```{{language}}\n{{code}}\n```\n\nFocus on:\n- Security issues\n- Performance\n- Best practices",
    "category": "development",
    "tags": ["code-review", "security", "performance"],
    "variables": [
      {"name": "language", "description": "Programming language", "type": "string", "required": true},
      {"name": "code", "description": "Code to review", "type": "string", "required": true}
    ]
  }'

Apply Template

curl -X POST http://localhost:3000/v1/prompts/code_review_assistant/apply \
  -H "Content-Type: application/json" \
  -d '{
    "language": "typescript",
    "code": "function hello() { console.log(\"Hello World\"); }"
  }'

🤖 MCP Integration

Model Context Protocol Support

MCP-Prompts implements the full Model Context Protocol specification, making it compatible with any MCP-compatible AI assistant or IDE.

MCP Server Modes

HTTP Mode (Default): REST API server
MCP Mode: Native MCP protocol server
CLI Mode: Command-line interface

Connecting to AI Assistants

Claude Desktop:

{
  "mcpServers": {
    "mcp-prompts": {
      "command": "npx",
      "args": ["@sparesparrow/mcp-prompts"],
      "env": {
        "STORAGE_TYPE": "aws",
        "AWS_REGION": "us-east-1"
      }
    }
  }
}

VS Code Extension:

{
  "mcp": {
    "servers": {
      "prompts": {
        "command": "npx",
        "args": ["@sparesparrow/mcp-prompts"],
        "options": {
          "env": {
            "STORAGE_TYPE": "aws",
            "AWS_REGION": "us-east-1"
          }
        }
      }
    }
  }
}

Available MCP Tools

list_prompts: Browse available prompts by category
get_prompt: Retrieve specific prompt with full details
create_prompt: Create new prompts programmatically
update_prompt: Modify existing prompts
delete_prompt: Remove prompts
apply_prompt: Apply template variables to generate final prompts

☁️ AWS Deployment

Architecture Overview

The AWS deployment creates a production-ready serverless architecture:

API Gateway - RESTful API endpoints with CORS
Lambda Functions - Serverless compute (MCP server, processing, catalog sync)
DynamoDB - NoSQL storage with global secondary indexes
S3 - Object storage for catalog and assets
SQS - Message queuing for async processing
CloudFront - Global CDN for performance
CloudWatch - Monitoring, logging, and metrics

Quick AWS Deployment

# Prerequisites: AWS CLI configured
aws configure

# One-command deployment
./scripts/deploy-aws-enhanced.sh

Manual AWS Deployment

# Install dependencies
pnpm install

# Build project
pnpm run build

# Deploy CDK infrastructure
cd cdk
pnpm install
cdk bootstrap aws://$AWS_ACCOUNT/$AWS_REGION
cdk deploy --all --require-approval never

AWS Configuration

AWS Permissions

The deployment creates minimal IAM roles following least-privilege:

Service	Permissions	Purpose
Lambda	CloudWatch Logs, VPC access	Function execution and logging
DynamoDB	Read/write access to tables	Prompt and session storage
S3	Read/write access to bucket	Catalog storage and assets
SQS	Send/receive/delete messages	Async processing queues
CloudWatch	PutMetricData, CreateLogGroups	Monitoring and metrics

AWS Cost Optimization

Free Tier Usage:

Lambda: 1M requests/month
DynamoDB: 25GB storage + 25 RCU/WCU
S3: 5GB storage + 20K GET + 2K PUT requests
API Gateway: 1M requests/month

Estimated Monthly Costs (beyond free tier):

$25-35/month for moderate usage
Scales with usage, no fixed costs

AWS Security Features

Encryption at rest (DynamoDB, S3)
Encryption in transit (HTTPS/TLS)
IAM roles with minimal permissions
CloudTrail audit logging
VPC support (optional)

📊 Monitoring

CloudWatch Metrics

Automatic custom metrics:

PromptAccessCount - Usage tracking by prompt/category
OperationLatency - API response times
ApiSuccess/ApiError - Success/error rates
ProcessingLatency - Background processing times
PromptCreated/Updated/Deleted - CRUD operations

Health Checks

# Check all services
curl https://your-api-gateway-url/health

# Expected response
{
  "status": "healthy",
  "services": {
    "dynamodb": {"status": "healthy"},
    "s3": {"status": "healthy"},
    "sqs": {"status": "healthy"}
  }
}

CloudWatch Alarms

Recommended alarms:

API error rate > 5%
Average latency > 1000ms
DynamoDB throttling events
Lambda error rate > 1%
SQS dead letter messages > 0

🔒 Security

Network Security

VPC Endpoints for private AWS service access
Security Groups restricting Lambda network access
WAF protection for API Gateway (optional)

Data Security

Encryption at rest for DynamoDB and S3
Encryption in transit via HTTPS/TLS
IAM roles following least-privilege principle
API Gateway authentication via Cognito (optional)

Secrets Management

AWS Systems Manager Parameter Store for configuration
AWS Secrets Manager for sensitive data
Environment variables for Lambda configuration

🧪 Testing

Local Development Testing

# Run unit tests
pnpm test

# Test with local DynamoDB
docker run -p 8000:8000 amazon/dynamodb-local
export DYNAMODB_ENDPOINT=http://localhost:8000
STORAGE_TYPE=memory pnpm run dev

Integration Testing

# Test health endpoint
curl http://localhost:3000/health

# Test prompts API
curl http://localhost:3000/v1/prompts

# Test prompt creation
curl -X POST http://localhost:3000/v1/prompts \
  -H "Content-Type: application/json" \
  -d '{"name": "test", "template": "Hello {{name}}", "category": "greeting"}'

AWS Integration Testing

# Test deployed API
curl https://your-api-gateway-url/health

# Test with AWS credentials
AWS_REGION=us-east-1 npx @sparesparrow/mcp-prompts list

🚨 Troubleshooting

Common Issues

AWS Deployment Issues

403 Access Denied
- Check IAM permissions for your AWS user/role
- Verify resource policies on DynamoDB/S3
- Check VPC configuration if using private subnets
CDK Bootstrap Required
```
cdk bootstrap aws://ACCOUNT/REGION
```
Lambda Timeout
- Increase timeout in CDK stack (default: 30s)
- Optimize cold start performance
- Check DynamoDB/S3 connection latency
DynamoDB Throttling
- Switch to On-Demand billing mode
- Optimize partition key distribution
- Implement exponential backoff in clients

Storage Issues

PostgreSQL Connection Failed
- Verify database credentials
- Check network connectivity
- Ensure database is running and accessible
File Storage Permissions
```
chmod -R 755 data/
```

MCP Integration Issues

MCP Server Not Connecting
- Verify environment variables are set
- Check AWS credentials for cloud storage
- Ensure correct MCP server mode

Debug Commands

AWS Services

# Check Lambda logs
aws logs describe-log-groups --log-group-name-prefix "/aws/lambda/McpPromptsStack"

# Check DynamoDB table
aws dynamodb describe-table --table-name mcp-prompts

# Check S3 bucket
aws s3 ls s3://your-bucket-name --recursive

# Check SQS queue
aws sqs get-queue-attributes --queue-url your-queue-url --attribute-names All

Local Development

# Check application logs
tail -f logs/mcp-prompts.log

# Test database connectivity
STORAGE_TYPE=postgres pg_isready -h localhost -p 5432

# Check Node.js processes
ps aux | grep mcp-prompts

Performance Tuning

Lambda Optimization

Increase memory allocation (more CPU)
Use provisioned concurrency for latency-sensitive functions
Optimize package size and dependencies

Database Optimization

Use appropriate indexes
Implement connection pooling
Monitor query performance

🔄 CI/CD Pipeline

GitHub Actions Workflows

The repository includes several CI/CD workflows:

CI/CD Pipeline (.github/workflows/ci-cd.yml)
- Tests on multiple storage types
- Builds and pushes Docker images
- Deploys to AWS
- Publishes to NPM
Multi-Platform Docker Build (.github/workflows/docker-multi-platform.yml)
- Builds for multiple architectures
- Pushes to GitHub Container Registry
AWS Deployment (.github/workflows/aws-deploy.yml)
- Automated AWS deployment
- Health checks and smoke tests

Required Secrets

Configure these secrets in your GitHub repository:

# AWS Deployment
AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY
AWS_REGION

# NPM Publishing
NPM_TOKEN

# Security Scanning
SNYK_TOKEN

Manual Deployment

# Install dependencies
pnpm install

# Build project
pnpm run build

# Run tests
pnpm test

# Deploy to AWS
./scripts/deploy-aws-enhanced.sh

# Or deploy to Docker
./scripts/deploy-docker.sh

📚 Further Reading

AWS Documentation

MCP Protocol

Development

🤝 Contributing

Development Setup

Fork the repository

Clone your fork

git clone https://github.com/your-username/mcp-prompts.git
cd mcp-prompts

Install dependencies
```
pnpm install
```

Create feature branch

git checkout -b feature/your-feature-name

Make changes and test
```
pnpm test
pnpm run build
```
Submit pull request

Guidelines

Follow existing code style and patterns
Add tests for new functionality
Update documentation for API changes
Use conventional commit messages
Test across all storage backends

Architecture Decisions

Hexagonal Architecture: Ports & Adapters pattern
Storage Adapters: Pluggable storage implementations
MCP Compliance: Full protocol implementation
TypeScript: Strict typing throughout

📄 License

MIT License - see LICENSE file for details.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

For Tasks:

Click tags to check more tools for each tasks

generate story ideas develop characters spark creativity overcome writer's block enhance storytelling skills

For Jobs:

writer content creator creative director copywriter journalist

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Daily Chinese ancient poetry and AI-generated images powered by Bing DALL-E-3. GitHub Action triggers the process automatically. Poetry is provided by Today's Poem API. The website is built with Astro.

github

: 492

exif-photo-blog

EXIF Photo Blog is a full-stack photo blog application built with Next.js, Vercel, and Postgres. It features built-in authentication, photo upload with EXIF extraction, photo organization by tag, infinite scroll, light/dark mode, automatic OG image generation, a CMD-K menu with photo search, experimental support for AI-generated descriptions, and support for Fujifilm simulations. The application is easy to deploy to Vercel with just a few clicks and can be customized with a variety of environment variables.

github

: 1.4k

SillyTavern

SillyTavern is a user interface you can install on your computer (and Android phones) that allows you to interact with text generation AIs and chat/roleplay with characters you or the community create. SillyTavern is a fork of TavernAI 1.2.8 which is under more active development and has added many major features. At this point, they can be thought of as completely independent programs.

github

: 18.8k

Twitter-Insight-LLM

This project enables you to fetch liked tweets from Twitter (using Selenium), save it to JSON and Excel files, and perform initial data analysis and image captions. This is part of the initial steps for a larger personal project involving Large Language Models (LLMs).

github

: 401

AISuperDomain

Aila Desktop Application is a powerful tool that integrates multiple leading AI models into a single desktop application. It allows users to interact with various AI models simultaneously, providing diverse responses and insights to their inquiries. With its user-friendly interface and customizable features, Aila empowers users to engage with AI seamlessly and efficiently. Whether you're a researcher, student, or professional, Aila can enhance your AI interactions and streamline your workflow.

github

: 1.2k

ChatGPT-On-CS

This project is an intelligent dialogue customer service tool based on a large model, which supports access to platforms such as WeChat, Qianniu, Bilibili, Douyin Enterprise, Douyin, Doudian, Weibo chat, Xiaohongshu professional account operation, Xiaohongshu, Zhihu, etc. You can choose GPT3.5/GPT4.0/ Lazy Treasure Box (more platforms will be supported in the future), which can process text, voice and pictures, and access external resources such as operating systems and the Internet through plug-ins, and support enterprise AI applications customized based on their own knowledge base.

github

: 768

obs-localvocal

LocalVocal is a live-streaming AI assistant plugin for OBS that allows you to transcribe audio speech into text and perform various language processing functions on the text using AI / LLMs (Large Language Models). It's privacy-first, with all data staying on your machine, and requires no GPU, cloud costs, network, or downtime.

github

: 248