SQS MCP Server: Give AI Agents Access to Queue Metrics, Messages, and Dead Letter Queues

Why Give AI Access to SQS?

AWS SQS processes millions of messages between your services. When queues back up, dead letter queues fill, consumers lag behind producers, or message attributes drift, debugging means navigating the SQS console, checking CloudWatch metrics, sampling messages, and correlating across multiple queues.

An MCP server for SQS lets your AI agent check queue depth, monitor dead letter queues, inspect message attributes, and track throughput directly from Claude, Cursor, or Windsurf through DataFaucet.

What Becomes Possible

Once your AI agent has SQS access:

• "How deep is the order-processing queue right now?"
• "Are there messages in the payment-dlq dead letter queue?"
• "What's the average message age in the notification queue?"
• "How many messages per second is the event-bus queue processing?"
• "Which queues have the longest consumer lag?"
• "What message attributes are on the items in the error queue?"

How It Works

1. Go to DataFaucet and enter your SQS console URL
2. Authenticate with your AWS IAM credentials
3. Browse queues, check metrics, inspect messages as you normally would
4. Each API call becomes an MCP tool your AI can invoke
5. Deploy and connect to Claude, Cursor, or Windsurf

The AI agent uses whatever SQS permissions your IAM role grants.

Security Best Practices

• Read-only IAM policy — use custom read-only policy (no managed read-only for SQS)
• No SendMessage/DeleteMessage — prevent AI from publishing or consuming messages
• No PurgeQueue — block queue purging through AI
• No CreateQueue/DeleteQueue — prevent infrastructure changes
• Queue-scoped ARNs — restrict to specific queue names or prefixes
• No ReceiveMessage with delete — use peek-only access via GetQueueAttributes

Use Cases by Role

Backend developers: Queue debugging. "Why is the order processor slow? What's the queue depth and consumer count?" while staying in the editor.

SRE/DevOps: Incident response. "Which queues are backing up? How many messages in DLQs? Is throughput dropping?" during alerts.

Platform engineers: Capacity planning. "Which queues are consistently near their throughput limit? Any candidates for FIFO conversion?"

Data engineers: Pipeline monitoring. "Is the ETL input queue draining? How old are the oldest messages? Any stuck batches?"

SQS Console vs AI Agent

Getting Started

Set up read-only access for your AI agent:

1. Create an IAM policy with read-only SQS access:

```json

{

"Effect": "Allow",

"Action": ["sqs:GetQueueAttributes", "sqs:GetQueueUrl",

"sqs:ListQueues", "sqs:ListQueueTags",

"sqs:ListDeadLetterSourceQueues",

"cloudwatch:GetMetricData"],

"Resource": "*"

}

```

1. Explicitly deny SendMessage, DeleteMessage, PurgeQueue, ReceiveMessage
2. Point DataFaucet at your SQS console
3. Authenticate with the read-only IAM credentials