AI agents call select_instance to retrieve information from QueryNest without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool appears to select a MongoDB instance for subsequent operations, presenting recommendations that require user confirmation. It is a selection/navigation action with no data modification. The description is partially uninformative (Chinese text with limited detail), so confidence is moderate.
From the tool's definition '智能实例选择工具：提供推荐选项，需要用户确认后执行' — selects/recommends an instance and requires user confirmation before execution
Attacks that exploit this kind of access
智能实例选择工具：提供推荐选项，需要用户确认后执行. It is categorised as a Read tool in the QueryNest MCP Server, which means it retrieves data without modifying state.
Register the QueryNest MCP server in PolicyLayer and add a rule for select_instance: 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 QueryNest. Nothing to install.
select_instance 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 select_instance 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 select_instance. 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.
select_instance is provided by the QueryNest MCP server (niuzaishu/querynest). 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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