Delete multiple planned training sessions at once (max 20). Returns per-session success/failure.
AI agents call delete_sessions to permanently remove resources in Pelaris — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
This tool permanently removes training session records from the fitness platform without recovery option. Although the data domain (fitness sessions) limits real-world damage compared to financial or security-critical systems, the destructive nature and potential to erase user workout history and progress tracking warrants the Destructive category.
From the tool's definition Tool name includes 'delete' and description states 'Delete multiple planned training sessions at once (max 20)'. Deletion is irreversible data destruction.
Attacks that exploit this kind of access
Delete multiple planned training sessions at once (max 20). Returns per-session success/failure. It is categorised as a Destructive tool in the Pelaris MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Pelaris MCP server in PolicyLayer and add a rule for delete_sessions: 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 Pelaris. Nothing to install.
delete_sessions is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the delete_sessions 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 delete_sessions. 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.
delete_sessions is provided by the Pelaris MCP server (thedonk/pelaris-mcp-server). 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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