MCP Server Setup

This guide explains how to set up and configure an MCP server optimized for infrastructure deployment and DevOps workflows.

What is MCP?

The Model Context Protocol (MCP) server acts as a middleware between LLMs and your development environment, providing:

  • Context-aware responses

  • Code analysis capabilities

  • Integration with development tools

  • Infrastructure-as-Code validation

  • Security scanning

Server Setup

Prerequisites

  • Docker and Docker Compose

  • 4GB RAM minimum

  • Git

  • Python 3.8+

Basic Installation

  1. Clone the MCP server repository:

git clone https://github.com/context-labs/mcp-server
cd mcp-server

  1. Create configuration file (config.yaml):

server:
  port: 3000
  host: "0.0.0.0"

models:
  - name: "claude-2"
    provider: "anthropic"
    config:
      api_key: ${ANTHROPIC_API_KEY}
  - name: "gpt-4"
    provider: "openai"
    config:
      api_key: ${OPENAI_API_KEY}

tools:
  - name: "terraform-validate"
    type: "shell"
    command: "terraform validate"
  - name: "terraform-plan"
    type: "shell"
    command: "terraform plan"
  - name: "aws-check"
    type: "shell"
    command: "aws sts get-caller-identity"

  1. Start the server:

docker-compose up -d

Infrastructure Tools Integration

Terraform Integration

tools:
  - name: "terraform-workflow"
    type: "composite"
    steps:
      - name: "validate"
        command: "terraform validate"
      - name: "security-scan"
        command: "tfsec ."
      - name: "plan"
        command: "terraform plan"
    
  - name: "terraform-docs"
    type: "shell"
    command: "terraform-docs markdown ."

Cloud Provider Integration

tools:
  - name: "aws-validate"
    type: "composite"
    steps:
      - name: "credentials-check"
        command: "aws sts get-caller-identity"
      - name: "policy-validation"
        command: "aws iam simulate-custom-policy"

  - name: "azure-validate"
    type: "composite"
    steps:
      - name: "login-check"
        command: "az account show"
      - name: "policy-check"
        command: "az policy state list"

Security Configuration

API Key Management

security:
  key_rotation:
    enabled: true
    interval: "7d"
  
  auth:
    type: "jwt"
    secret: ${JWT_SECRET}
    
  rate_limiting:
    enabled: true
    requests_per_minute: 60

Logging Configuration

logging:
  level: "info"
  format: "json"
  outputs:
    - type: "file"
      path: "/var/log/mcp-server.log"
    - type: "cloudwatch"
      group: "mcp-logs"
      region: "us-west-2"

Best Practices

  1. Resource Management

    • Monitor memory usage

    • Implement rate limiting

    • Use connection pooling

    • Scale horizontally when needed

  2. Security

    • Regular key rotation

    • JWT authentication

    • Request validation

    • Input sanitization

  3. Monitoring

    monitoring:
  prometheus:
    enabled: true
    port: 9090
  alerts:
    - name: "high_memory"
      threshold: "85%"
      action: "notify"

Example Use Cases

Infrastructure Validation

from mcp_client import MCPClient

client = MCPClient("http://localhost:3000")
result = client.validate_infrastructure("""
resource "aws_s3_bucket" "example" {
  bucket = "my-bucket"
  acl    = "private"
}
""")

Security Scanning

scan_result = client.security_scan({
    "type": "terraform",
    "path": "./infrastructure",
    "checks": ["CKV_AWS_*"]
})

Troubleshooting

  1. Connection Issues

    • Check network configurations

    • Verify port mappings

    • Validate TLS certificates

  2. Performance Issues

    • Monitor resource usage

    • Check logging levels

    • Analyze request patterns

  3. Tool Integration Issues

    • Verify tool installations

    • Check PATH configurations

    • Validate permissions

Resources

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