AI agents call get_env to retrieve information from LuzzyTool without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves environment variable values without altering or executing any operations. It is categorized as Read because it only queries and returns data. Severity is medium rather than low because environment variables can contain sensitive information (API keys, credentials, paths) that could be leveraged by a malicious agent, but the tool itself performs no destructive or executable actions.
From the tool's definition Tool name 'get_env' and description '【查询操作】获取环境变量的当前值' (Query operation: get the current value of environment variables) explicitly indicate a retrieval/query action with no modifications.
Attacks that exploit this kind of access
【查询操作】获取环境变量的当前值。. It is categorised as a Read tool in the LuzzyTool MCP Server, which means it retrieves data without modifying state.
Register the LuzzyTool MCP server in PolicyLayer and add a rule for get_env: 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 LuzzyTool. Nothing to install.
get_env 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 get_env 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 get_env. 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.
get_env is provided by the LuzzyTool MCP server (luzzymeow/luzzytool). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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