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Labs64

Labs64/NetLicensing-MCP

netlicensing_delete_product_module

Remove a product module from NetLicensing, optionally deleting associated license templates and licenses in cascade.

Instructions

Delete a product module.

Args: module_number: Module to delete force_cascade: Also delete all dependent license templates and licenses

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
module_numberYes
force_cascadeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions the 'force_cascade' parameter's effect ('delete all dependent license templates and licenses'), which adds some behavioral context. However, it lacks critical details: whether deletion is permanent/reversible, required permissions, error conditions (e.g., if dependencies exist without cascade), or rate limits. For a destructive operation with no annotations, this is insufficient.

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

Conciseness4/5

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

The description is brief and structured with a clear purpose statement followed by parameter explanations. Every sentence adds value: the first states the action, and the next two clarify parameters. It could be more front-loaded by integrating parameter hints into the main sentence, but there's no wasted text.

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?

Given the tool's complexity (destructive delete with cascade option), no annotations, and an output schema (which reduces need to describe returns), the description is moderately complete. It covers the basic action and parameters but misses important context: safety warnings, idempotency, or example usage. The output schema likely handles return values, but behavioral gaps remain.

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 description coverage is 0%, so the description must compensate. It explains both parameters: 'module_number' identifies the target, and 'force_cascade' controls dependency deletion. This adds meaningful semantics beyond the bare schema (which only shows types and titles). However, it doesn't clarify parameter formats (e.g., what a module number looks like) or default behavior when force_cascade is false.

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

Purpose4/5

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

The description clearly states the action ('Delete') and resource ('a product module'), making the purpose unambiguous. It distinguishes from sibling tools by specifying the exact resource type (product module) rather than other entities like bundles, licenses, or licensees. However, it doesn't explicitly contrast with similar delete operations for other resources beyond naming the tool.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., whether the module must exist or be inactive), consequences of deletion, or comparisons to other delete tools in the sibling list. The agent must infer usage from the tool name alone.

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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