Frequently Asked Questions¶

Common questions and answers about the MCP Template Platform.

General Questions¶

What is the MCP Template Platform?¶

The MCP Template Platform is a comprehensive system for deploying and managing Model Context Protocol (MCP) servers. It provides pre-built templates for common integrations, a CLI tool for easy deployment, and tools for creating custom MCP servers.

Key Benefits: - 🚀 One-command deployment of MCP servers - 📦 Pre-built templates for popular services - 🔧 Template creation tools for custom integrations - 🐳 Docker-based for consistent deployments - 🔍 Automatic tool discovery using MCP protocol

How does it relate to the Model Context Protocol?¶

The platform implements the official Model Context Protocol specification. It provides:

MCP Server Templates: Pre-configured servers that expose tools via MCP
Protocol Implementation: Full MCP 2025-06-18 specification support
Tool Discovery: Automatic detection of available MCP tools
Client Integration: Ready-to-use configurations for LLM clients

Is this officially associated with Anthropic?¶

No, this is an independent open-source project that implements the MCP specification. While it follows the official MCP protocol, it's not developed or endorsed by Anthropic.

Getting Started¶

What are the system requirements?¶

Minimum Requirements: - Python 3.8+ - Docker 20.10+ - 2GB RAM - 10GB disk space

Recommended: - Python 3.11+ - Docker 24.0+ - 4GB RAM - 20GB disk space

Operating System Support: - ✅ Linux (all distributions) - ✅ macOS 10.15+ - ✅ Windows 10+ (with WSL2)

How do I install the platform?¶

# Install from PyPI
pip install mcp-templates

# Verify installation
mcp-template --version

# Test with demo template
mcp-template deploy demo

See the Installation Guide for detailed instructions.

What templates are available?¶

Popular Templates: - file-server: Secure filesystem access - demo: Basic demonstration server - github: GitHub API integration - database: SQL database connectivity

Full List:

mcp-template list

View detailed information in the Template Library.

Template Usage¶

How do I deploy a template?¶

# Basic deployment
mcp-template deploy template-name

# With configuration
mcp-template deploy file-server --config base_path=/home/user/documents

# With config file
mcp-template deploy database --config-file db-config.json

How do I configure templates?¶

Three ways to configure templates:

Command-line options:

mcp-template deploy template --config key=value

Configuration file:

mcp-template deploy template --config-file config.json

Environment variables:

export MCP_API_KEY="your-key"
mcp-template deploy template

Configuration precedence: Environment Variables > CLI Options > Config File > Template Defaults

How do I connect templates to AI assistants?¶

Claude Desktop:

# Generate configuration
mcp-template connect template-name --llm claude

# Add to Claude Desktop config
# (~/.config/claude-desktop/claude_desktop_config.json)

VS Code:

# Generate VS Code configuration
mcp-template connect template-name --llm vscode

Custom Integration:

# Get JSON configuration
mcp-template tools template-name --format json

How do I see what tools are available?¶

# List tools in a template
mcp-template tools template-name

# Discover tools from any MCP server
mcp-template tools --image custom/mcp-server

# Get detailed tool information
mcp-template tools template-name --detailed

Template Development¶

How do I create a custom template?¶

Interactive Creation:

mcp-template create my-custom-template
# Follow the prompts for configuration

From Existing Image:

mcp-template create --from-image existing/mcp-server my-template

Manual Creation: See the Template Development Guide for detailed instructions.

What files does a template need?¶

Required Files:

templates/my-template/
├── template.json      # Template metadata and configuration schema
├── Dockerfile         # Container build instructions
└── README.md          # Documentation (recommended)

Recommended Structure:

templates/my-template/
├── template.json      # Template configuration
├── Dockerfile         # Container definition
├── README.md          # Template documentation
├── src/              # Source code
│   ├── server.py     # MCP server implementation
│   └── tools.py      # Tool implementations
├── config/           # Configuration examples
├── tests/            # Test suite
└── docs/             # Additional documentation

How do I test my template?¶

# Validate template structure
mcp-template validate my-template

# Deploy for testing
mcp-template deploy my-template

# Test tool discovery
mcp-template tools my-template

# Run template tests
cd templates/my-template
python -m pytest tests/

Can I use languages other than Python?¶

Yes! Templates can use any language that supports the MCP protocol:

Supported Languages: - ✅ Python (FastMCP, mcp-python) - ✅ TypeScript/JavaScript (@modelcontextprotocol/sdk) - ✅ Go (community implementations) - ✅ Rust (community implementations)

The key requirements: 1. Implement MCP JSON-RPC over stdio 2. Support MCP protocol 2025-06-18 3. Containerized with Docker

Deployment & Operations¶

How do I manage multiple deployments?¶

# List all deployments
mcp-template list

# Check deployment status
mcp-template status

# View specific deployment
mcp-template status deployment-name

# Stop deployment
mcp-template stop deployment-name

# Remove deployment
mcp-template delete deployment-name

How do I monitor deployments?¶

# View logs
mcp-template logs deployment-name

# Follow logs in real-time
mcp-template logs deployment-name --follow

# Monitor status continuously
mcp-template status --watch

# Health check only
mcp-template status --health-only

How do I update deployments?¶

# Update to latest image
mcp-template deploy template-name --force-pull

# Force recreate container
mcp-template deploy template-name --force-recreate

# Update with new configuration
mcp-template deploy template-name --config new_setting=value

Where are deployment data and logs stored?¶

Default Locations: - Data: ~/mcp-data/ (mapped to /data in container) - Logs: ~/.mcp/logs/ (mapped to /logs in container) - Config: ~/.mcp/config/

Custom Locations:

# Use custom data directory
mcp-template deploy template --volume /custom/path:/data

# Multiple volumes
mcp-template deploy template \
  --volume /data1:/app/data1 \
  --volume /data2:/app/data2

Troubleshooting¶

My deployment failed to start. What should I check?¶

Check logs:
```
mcp-template logs deployment-name
```
Verify Docker:
```
docker --version
docker info
```
Check configuration:
```
mcp-template config template-name
```

Test image directly:

docker run -it template-image:latest /bin/bash

See the Troubleshooting Guide for comprehensive solutions.

Tools aren't being discovered. Why?¶

Common Causes: 1. MCP server not responding: Check container logs 2. Wrong transport protocol: Try --transport stdio or --transport http 3. Container startup issues: Verify container is running 4. Configuration errors: Check environment variables

Debugging Steps:

# Test tool discovery directly
mcp-template tools --image template:latest

# Check MCP protocol response
mcp-template connect deployment --test-connection

# Monitor container startup
mcp-template logs deployment --follow

How do I get help with specific issues?¶

Check Documentation:
CLI Reference
Troubleshooting Guide
Template Development Guide
Community Support:
GitHub Issues: Report bugs and feature requests
GitHub Discussions: Ask questions and share solutions
Community Slack: Join mcp-platform workspace for real-time community chat
Professional Support:
Enterprise support available
Custom template development services
Contact: support@dataeverything.ai

Performance & Scaling¶

How many deployments can I run?¶

Typical Limits: - Development: 5-10 deployments per machine - Production: 50+ deployments with proper resource management

Resource Planning: - Each deployment: ~100-500MB RAM - CPU usage: Minimal when idle - Disk: Depends on data volumes

How do I optimize performance?¶

Template Level:

# Set resource limits
mcp-template deploy template --memory 512m --cpu 0.5

# Use efficient base images
# In Dockerfile: FROM python:3.11-slim instead of python:3.11

System Level:

# Clean up unused resources
docker system prune -f

# Monitor resource usage
docker stats

# Use Docker BuildKit for faster builds
export DOCKER_BUILDKIT=1

Can I run this in production?¶

Yes! The platform supports production deployments:

Production Features: - Health Monitoring: Built-in health checks and status monitoring - Logging: Comprehensive logging with rotation - Resource Management: Memory and CPU limits - Security: Container isolation and network security - Backup: Configuration and data backup support

Production Recommendations: - Use Docker Compose or Kubernetes for orchestration - Set up monitoring and alerting - Implement backup strategies - Use resource limits - Regular security updates

Security¶

Is it safe to run MCP servers?¶

The platform follows security best practices:

Security Features: - Container Isolation: Each deployment runs in isolated Docker containers - No Root Access: Containers run as non-root users - Network Isolation: Minimal network exposure - Resource Limits: Prevents resource exhaustion - Secret Management: Environment variable-based configuration

Security Best Practices: - Keep images updated - Use minimal base images - Limit network access - Regular security audits - Secure secret storage

How do I handle sensitive configuration?¶

Environment Variables:

# Use environment variables for secrets
export MCP_API_KEY="secret-key"
mcp-template deploy template

Config Files with Restricted Permissions:

# Create secure config file
echo '{"api_key": "secret"}' > config.json
chmod 600 config.json
mcp-template deploy template --config-file config.json

External Secret Management:

# Use external secret managers
export MCP_API_KEY=$(vault kv get -field=key secret/mcp/api)
mcp-template deploy template

Integration & Compatibility¶

What AI assistants work with this?¶

Officially Supported: - ✅ Claude Desktop (Anthropic) - ✅ VS Code (with MCP extensions) - ✅ Continue.dev - ✅ Custom Python applications

Community Supported: - ⚠️ Other LLM clients (varies by MCP support)

Can I integrate with existing systems?¶

Yes! The platform provides multiple integration options:

API Integration:

from mcp_template import TemplateManager

manager = TemplateManager()
deployment = manager.deploy("template-name", config={"key": "value"})
tools = manager.discover_tools(deployment)

CLI Integration:

# Scriptable CLI interface
mcp-template deploy template --format json

Docker Integration:

# Direct Docker usage
docker run -d --name mcp-server template:latest

Does it work with Kubernetes?¶

While primarily designed for Docker, you can adapt for Kubernetes:

Manual Kubernetes Deployment:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: mcp-template
spec:
  replicas: 1
  selector:
    matchLabels:
      app: mcp-template
  template:
    metadata:
      labels:
        app: mcp-template
    spec:
      containers:
      - name: mcp-server
        image: template:latest
        env:
        - name: MCP_CONFIG
          value: "production"

Future Support: Kubernetes backend support is planned for future releases.

Contributing¶

How can I contribute to the project?¶

Ways to Contribute: 1. Report Issues: Bug reports and feature requests 2. Create Templates: Share useful MCP server templates 3. Improve Documentation: Fix errors, add examples 4. Code Contributions: Platform improvements and new features 5. Community Support: Help other users in discussions

Getting Started: 1. Fork the repository 2. Create a feature branch 3. Make your changes 4. Add tests 5. Submit a pull request

See the Contributing Guide for detailed guidelines.

What templates are most needed?¶

High Priority: - Popular APIs: Stripe, Twilio, SendGrid - Database Connectors: MongoDB, Redis, Elasticsearch - Cloud Services: AWS, Google Cloud, Azure - Development Tools: Jira, Linear, Notion

Template Ideas: - Custom business integrations - Industry-specific tools - Regional service providers - Niche technical tools

How do I submit a new template?¶

Create the template:
```
mcp-template create my-new-template
```

Test thoroughly:

mcp-template deploy my-new-template
mcp-template tools my-new-template

Add documentation:
Complete README.md
Usage examples
Configuration guide
Submit pull request:
Include template in templates/ directory
Add tests
Update template registry

Commercial Usage¶

Can I use this commercially?¶

Yes! The MCP Template Platform is open source under the MIT License, which allows commercial use.

Commercial Usage Rights: - ✅ Use in commercial products - ✅ Modify and distribute - ✅ Private use - ✅ Commercial distribution

Requirements: - Include license notice - No warranty provided

Do you offer commercial support?¶

Yes, commercial support is available:

Enterprise Support: - Priority bug fixes - Custom template development - Training and consulting - SLA guarantees

Professional Services: - Custom integration development - Architecture consulting - Team training - Production deployment assistance

Contact: enterprise@dataeverything.ai

Can I create paid templates?¶

While the core platform is open source, you can:

Create proprietary templates for internal use
Offer template development services
Build commercial products using the platform
Provide support and consulting services

The template ecosystem encourages both open source and commercial contributions.

Still have questions?

📖 Check the full documentation
💬 Join our community discussions
🐛 Report issues
📧 Contact us: support@dataeverything.ai