lake_query
AI agents call lake_query to retrieve information from Awslabs Valkey without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Based on the tool name alone, 'lake_query' appears to be a read operation querying data from a data lake or cache. Despite the empty description, the naming convention suggests retrieval rather than modification or deletion. However, confidence is reduced to 0.6 due to lack of descriptive information.
From the tool's definition Tool name 'lake_query' suggests querying operations. Description is empty, limiting confidence.
Attacks that exploit this kind of access
lake_query. It is categorised as a Read tool in the Awslabs Valkey MCP Server, which means it retrieves data without modifying state.
Register the Awslabs Valkey 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 Awslabs Valkey. Nothing to install.
lake_query is a Read tool with low risk. Read-only tools are generally safe to allow by default.
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 Awslabs Valkey MCP server (awslabs.valkey-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.