AI agents invoke lake_query to trigger actions in AWS Documentation 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 'lake_query' strongly implies executing queries against a data lake service. Depending on what SQL/queries are permitted, this could range from Read to Destructive. With no description available, the most defensible classification is Execute, as arbitrary query execution against a data lake can have significant side effects. Confidence is lowered due to the empty description.
From the tool's definition Tool name 'lake_query' — the word 'query' suggests executing queries, likely against a data lake (e.g., AWS Lake Formation or S3/Athena). Description is empty and uninformative.
Documented attack patterns abuse exactly the kind of access lake_query gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and AWS Documentation MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for lake_query:
{
"version": "1",
"default": "deny",
"tools": {
"lake_query": {
"limits": [
{
"counter": "lake_query_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}lake_query 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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lake_query. It is categorised as a Execute tool in the AWS Documentation MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the AWS Documentation MCP Server MCP server in PolicyLayer and add a rule for lake_query: 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 AWS Documentation MCP Server. Nothing to install.
lake_query 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 lake_query 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 lake_query. 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.
lake_query is provided by the AWS Documentation MCP Server MCP server (awslabs.aws-documentation-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from AWS Documentation MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
805 AWS Documentation MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.