Execution roadmap: group deliveries into themed work scopes. Focuses define what the AI daemon works on -- assigning an agent role queues the focus for autonomous execution. Actions: list focuses for a product, create a focus (name + productId), update focus settings (description, status, agent r...
Risk signalsHigh parameter count (30 properties)
Part of the Mcp Products server.
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AI agents may call telora_product_focus to permanently remove or destroy resources in Mcp Products. Without a policy, an autonomous agent could delete critical data in a loop with no way to undo the damage. PolicyLayer blocks destructive tools by default and requires explicit human approval before enabling them.
Without a policy, an AI agent could call telora_product_focus in a loop, permanently destroying resources in Mcp Products. There is no undo for destructive operations. PolicyLayer blocks this tool by default and only allows it when a human explicitly approves the action.
Destructive tools permanently remove data. Block by default. Only enable with explicit approval workflows.
{
"version": "1",
"default": "deny",
"hide": [
"telora_product_focus"
]
}See the full Mcp Products policy for all 14 tools.
These attack patterns abuse exactly the kind of access telora_product_focus gives an agent. Each links to the full case and the policy that stops it:
Other destructive tools across the catalogue. The same approach applies to each: deny by default, or require human approval.
Execution roadmap: group deliveries into themed work scopes. Focuses define what the AI daemon works on -- assigning an agent role queues the focus for autonomous execution. Actions: list focuses for a product, create a focus (name + productId), update focus settings (description, status, agent role, pipeline config), reorder focus priority, merges (list merge history for a focus), retrospective_trigger (create a pending retrospective for a focus), retrospective_run (execute a pending retrospective via LLM), retrospective_list (list retrospectives for a focus), retrospective_get (get a single retrospective by ID), review_complete (terminal action for a review agent: writes a focus_reviews row with outcome + summary + references, and updates focus.last_reviewed_at and focus.last_review_outcome). On update, pass clearReviewRequest: true to clear product_focuses.review_requested_at -- the non-SQL recovery path for a focus stuck with a stale review request. List responses include a 1-based index on each item (response-local; recomputed per call).. It is categorised as a Destructive tool in the Mcp Products MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Mcp Products MCP server in PolicyLayer and add a rule for telora_product_focus: 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 Mcp Products. Nothing to install.
telora_product_focus 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 telora_product_focus 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 telora_product_focus. 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.
telora_product_focus is provided by the Mcp Products MCP server (@telora/mcp-products). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 14 Mcp Products tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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