AI agents invoke browser_close 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.
The name 'browser_close' implies terminating a browser process or session, which is an external execution-side effect. However, the description is completely empty, so the exact behavior is unknown. Closing a browser could disrupt active sessions or workflows, warranting a high severity.
From the tool's definition Tool name 'browser_close' suggests closing a browser instance — an external operation. Description is empty and uninformative, lowering confidence.
Documented attack patterns abuse exactly the kind of access browser_close 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 browser_close:
{
"version": "1",
"default": "deny",
"tools": {
"browser_close": {
"limits": [
{
"counter": "browser_close_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
} browser_close 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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browser_close. 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 browser_close: 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.
browser_close 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 browser_close 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 browser_close. 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.
browser_close 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.