call_aws
AI agents invoke call_aws to trigger actions in Amazon MQ MCP Server. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
The name 'call_aws' strongly implies execution of arbitrary AWS API calls. Given the server manages AWS infrastructure (AmazonMQ brokers), this tool likely can invoke a wide range of AWS operations including destructive or financial ones. Empty description lowers confidence, but the blast radius is critical given potential access to AWS APIs.
From the tool's definition Tool name 'call_aws' on a server described as managing AMQ brokers; description is empty/uninformative
Attacks that exploit this kind of access
call_aws. It is categorised as a Execute tool in the Amazon MQ MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Amazon MQ MCP Server MCP server in PolicyLayer and add a rule for call_aws: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Amazon MQ MCP Server. Nothing to install.
call_aws is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the call_aws rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for call_aws. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
call_aws is provided by the Amazon MQ MCP Server MCP server (awslabs.amazon-mq-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.