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hshen-ai

zscaler-aiguard-control-plane-mcp

by hshen-ai

delete_policy

Permanently delete a detection policy and optionally cascade-delete all referencing match rules and LLM assignments to clean up dependencies.

Instructions

Permanently deletes a detection policy. Set delete_references=True to cascade-delete all referencing match rules and LLM assignments.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
policy_idYes
delete_referencesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Without annotations, the description carries the full burden. It clearly states that deletion is permanent and explains the cascade behavior via the delete_references parameter. However, it does not disclose what happens if delete_references is false and references exist, or any authorization requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence. It front-loads the primary purpose and then adds the key optional behavior. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While the description covers the core action and a key parameter, it fails to address what happens if a policy with references is deleted without setting delete_references=true. This is a significant gap for a mutation tool with no annotations. The presence of an output schema reduces the need to explain return values, but the incomplete behavior disclosure lowers the score.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It adds meaning to delete_references by explaining its effect ('cascade-delete all referencing match rules and LLM assignments'). policy_id lacks additional context but is self-explanatory. Overall, it adds value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Permanently deletes') and the resource ('detection policy'), with an additional detail about cascade deletion. It effectively distinguishes this from sibling tools like create_policy or update_policy.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for permanent deletion but does not explicitly state when to use vs alternatives (e.g., deactivate) or when not to use it. No prerequisites or error conditions are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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