MCP Prompts Server

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

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

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. 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.

Copyright (c) 2024 Sparre Sparrow

Support

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

Resources

• Model Context Protocol
• MCP Specification
• AWS Documentation
• TypeScript Docs

Acknowledgments

Built with:

• @modelcontextprotocol/sdk
• Express
• AWS SDK v3
• TypeScript
• Zod

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