Get stale tensors based on a threshold of days and limit.
AI agents call analytics_get_stale_tensors to retrieve information from Tensorus MCP without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves or lists tensors matching staleness criteria (age threshold and limit parameters). It performs a read-only analytics query against the tensor database. There is no creation, modification, deletion, or execution of arbitrary code. The blast radius of misuse is minimal—returning metadata about old tensors poses no financial risk, irreversible data loss, or external system impact.
From the tool's definition Tool name 'analytics_get_stale_tensors' and description 'Get stale tensors based on a threshold of days and limit' indicate a query/retrieval operation with no modification or deletion of data.
Attacks that exploit this kind of access
Get stale tensors based on a threshold of days and limit. It is categorised as a Read tool in the Tensorus MCP MCP Server, which means it retrieves data without modifying state.
Register the Tensorus MCP server in PolicyLayer and add a rule for analytics_get_stale_tensors: 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 Tensorus MCP. Nothing to install.
analytics_get_stale_tensors 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 analytics_get_stale_tensors 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 analytics_get_stale_tensors. 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.
analytics_get_stale_tensors is provided by the Tensorus MCP server (tensorus/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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