AI agents invoke call_aws to trigger actions in Amazon Location Service 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.
With an empty description, the exact behavior is unknown, but 'call_aws' implies arbitrary AWS API invocation. Given the context of an AWS MCP server with sibling tools that perform a wide range of operations (policy management, user management, etc.), this tool likely executes AWS SDK/API calls with parameters determined at runtime.
From the tool's definition Tool name is 'call_aws' with empty description. The name suggests it invokes AWS API calls, which could span read, write, execute, destructive, or financial operations depending on arguments.
Documented attack patterns abuse exactly the kind of access call_aws gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Amazon Location Service MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for call_aws:
{
"version": "1",
"default": "deny",
"tools": {
"call_aws": {
"limits": [
{
"counter": "call_aws_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
} call_aws stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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call_aws. It is categorised as a Execute tool in the Amazon Location Service MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Amazon Location Service 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 Location Service 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 Location Service MCP Server MCP server (awslabs.aws-location-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Amazon Location Service MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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805 Amazon Location Service MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.