4 MCPs Every Backend Dev Should Install Today (Part 2)

Your AI assistant helps with code, but it's blind to your actual systems. It hallucinates database schemas. Suggests MongoDB operators that don't exist. Writes CloudFormation that fails on deploy.

Here are 4 MCP servers that fix this:

1. Postgres MCP - Your AI sees your actual database schema
2. MongoDB MCP - Official MongoDB support for natural language queries
3. Postman MCP - Manage collections and environments via AI
4. AWS MCP - Infrastructure as code through natural language

1. Postgres MCP: No More Schema Guessing

The problem: You ask AI for a database query. It guesses. Column doesn't exist. You check pgAdmin, fix manually. Five minutes gone. Repeat 50 times daily.

Postgres MCP gives your AI direct database access. Three options:

Read-Only (Production Safe):

{
  "mcpServers": {
    "postgres": {
      "command": "docker",
      "args": ["run","-i","--rm","-e","POSTGRES_URL","mcp/postgres"],
      "env": {"POSTGRES_URL": "postgresql://host.docker.internal:5432/mydb"}
    }
  }
}

Full Access: CrystalDBA's Postgres MCP Pro with unrestricted/restricted modes.

Supabase: Their Remote MCP - paste URL, authenticate, done.

Real Impact: Finding slow queries. Old way: SSH, query pg_stat_statements, run EXPLAIN, guess index. 45 minutes. With MCP: "Show me the slowest queries" → AI identifies missing index → "Add it" → Done. 3 minutes.

Warning: Large schemas consume 10k+ tokens. Be specific with queries.

2. MongoDB MCP: Stop Writing Pipelines From Memory

The problem: Writing aggregation pipelines. Open docs, copy example, modify, test, fail, check syntax, realize $group comes before $match. Your AI suggests operators that don't exist.

MongoDB MCP Server - official from MongoDB Inc. 22 tools including aggregations, schema inspection, Atlas management.

Setup (Local):

{
  "mcpServers": {
    "MongoDB": {
      "command": "npx",
      "args": ["-y","mongodb-mcp-server","--connectionString",
               "mongodb://localhost:27017/myDatabase","--readOnly"]
    }
  }
}

For Atlas, add API credentials. Remove --readOnly for development databases.

Real Impact: Building analytics endpoint. Old way: copy pipeline example, modify, test in Compass, fix syntax, debug field names. 25 minutes per pipeline. With MCP: "Group orders by region, sum revenue, return top 5" → AI checks schema, generates correct pipeline. 45 seconds.

Your AI becomes a database analyst that knows your actual data structure.

3. Postman MCP: API Management Without Clicking

The problem: Building an endpoint. Create collection in Postman. Set environment variables. Write tests. Switch to code. Update API. Switch back. Update collection. 20 clicks for one command.

Postman MCP Server - official from Postman Labs. 38 base tools, 100+ in full mode.

Setup (Docker MCP Toolkit):

docker mcp client connect cursor -g
docker mcp server enable postman
# Add Postman API key in Docker MCP UI

Real Impact: Syncing OpenAPI specs from Django REST Framework. Old way: generate spec, export JSON, import to Postman, update collection, check endpoints. 15 minutes per API change. With MCP: "Sync my Django OpenAPI spec with Postman collection" → Done. 30 seconds.

Built-in tools: syncCollectionWithSpec and syncSpecWithCollection keep everything synchronized automatically.

4. AWS MCP: Infrastructure Without YAML

The problem: Need an S3 bucket with versioning, KMS encryption, CloudFront. Either click through console or write CloudFormation/Terraform. 30 minutes for something that should take 30 seconds.

AWS Cloud Control API MCP Server - official from AWS Labs. Manages 1,200+ AWS resources through natural language.

Features:

• Outputs Infrastructure as Code templates
• AWS Pricing API for cost estimates
• Security scanning with Checkov
• Read-only mode for production

Setup:

{
  "mcpServers": {
    "awslabs.ccapi-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.ccapi-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "your-profile",
        "SECURITY_SCANNING": "enabled"
      }
    }
  }
}

Add --readonly for production accounts.

Real Impact: Deploying serverless API (API Gateway + Lambda + DynamoDB). Old way: write CloudFormation, define resources, configure IAM, test, debug permissions. 2+ hours. With MCP: "Create a serverless API" → AI creates infrastructure, runs security scan, shows cost estimate ($0.20/month), deploys. 2 minutes.

Cost Protection: Before creating resources, AI shows monthly estimates. No surprise NAT Gateway bills.

CI/CD Ready: Outputs CloudFormation/Terraform code. Natural language for development, IaC for production pipelines.

Azure has official Microsoft MCP. GCP has community servers. Same workflow, slightly less features.

The Pattern

Each tool follows the same principle: Your AI stops guessing and starts seeing. Real schemas. Real collections. Real infrastructure.

You still need expertise - understanding indexes, API design, AWS services. These tools just remove the friction between your knowledge and execution.

Time saved per day:

• Postgres: 10 schema lookups × 5 minutes = 50 minutes
• MongoDB: 5 pipelines × 20 minutes = 100 minutes
• Postman: 8 collection updates × 10 minutes = 80 minutes
• AWS: 3 infrastructure changes × 30 minutes = 90 minutes

That's 5+ hours daily on context switching and manual lookups.

Install One Now

You just spent 5 minutes reading this. Most people will close the tab and do nothing.

Pick one. Install it. Use it today. Track the time saved.

The developers winning with AI aren't waiting for AGI. They're connecting their AI to their actual systems right now.