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lake_query

lake_query

How to control lake_query ↓

What lake_query does on AWS Support MCP Server

AI agents invoke lake_query to trigger actions in AWS Support 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.

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Why lake_query needs a policy

The name 'lake_query' implies executing queries against a data lake. Queries can range from read-only SELECT statements to potentially destructive or data-modifying operations depending on arguments. Given the empty description, confidence is low, but 'query' operations against data lakes often involve executing SQL-like statements that could have significant effects.

From the tool's definition Tool name 'lake_query' suggests querying a data lake (e.g., AWS Lake Formation or S3-based data lake), but the description is empty and uninformative.

Documented attack patterns abuse exactly the kind of access lake_query gives an agent:

How to control lake_query

PolicyLayer is an MCP gateway — it sits between your AI agents and AWS Support MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for lake_query:

policy.json
{
  "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.

  1. Create a free account and register AWS Support MCP Server — nothing to install.
  2. Add this policy — paste it, or build it visually.
  3. Point your MCP client (Claude, Cursor, anything) at your gateway URL.
RATE-LIMIT THIS TOOL →

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Related tools and policies

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Questions about lake_query

What does the lake_query tool do? +

lake_query. It is categorised as a Execute tool in the AWS Support MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on lake_query? +

Register the AWS Support 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 Support MCP Server. Nothing to install.

What risk level is lake_query? +

lake_query is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit lake_query? +

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.

How do I block lake_query completely? +

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.

What MCP server provides lake_query? +

lake_query is provided by the AWS Support MCP Server MCP server (awslabs.aws-support-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every AWS Support MCP Server tool call.

Start from AWS Support 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 Support MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.

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