Aggregate tensor metadata based on a grouping field and aggregation function.
AI agents call aggregate_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.
Aggregation over metadata is a read/query operation that groups and summarizes existing data without modifying, creating, or deleting anything. No side effects are described.
From the tool's definition Aggregate tensor metadata based on a grouping field and aggregation function
Attacks that exploit this kind of access
Aggregate tensor metadata based on a grouping field and aggregation function. 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 aggregate_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.
aggregate_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 aggregate_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 aggregate_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.
aggregate_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.
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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