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Labs64/NetLicensing-MCP

netlicensing_delete_license_template

Delete a license template from NetLicensing, optionally removing all dependent licenses with cascade deletion.

Instructions

Delete a license template.

Args: template_number: Template to delete force_cascade: Also delete all dependent licenses

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
template_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 states the tool deletes a license template and mentions cascading deletion via 'force_cascade', which hints at destructive behavior. However, it lacks critical details: whether deletion is permanent, what permissions are required, if there are rate limits, or what happens to dependent data without cascading. This is inadequate for a mutation tool with no annotation coverage.

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 front-loaded with the core purpose ('Delete a license template.'), followed by parameter explanations. It avoids unnecessary words, but the two-sentence structure could be more integrated. Every sentence adds value, though it's slightly terse given the tool's complexity.

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 has an output schema (not provided here), the description needn't explain return values. However, as a destructive operation with no annotations and 2 parameters, it should provide more behavioral context (e.g., permanence, error conditions). The parameter explanations help, but overall completeness is minimal for a delete tool in a system with many siblings.

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: 'template_number' identifies the template to delete, and 'force_cascade' controls deletion of dependent licenses. This adds meaningful context beyond the schema's basic types. However, it doesn't specify formats (e.g., what a template number looks like) or elaborate on cascade implications, leaving some ambiguity.

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 ('license template'), making the purpose immediately understandable. It distinguishes itself from siblings like 'netlicensing_update_license_template' by specifying deletion rather than modification. However, it doesn't explicitly differentiate from other delete operations (e.g., 'netlicensing_delete_license'), though the resource type is implied.

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 template must exist or be unused), compare it to other delete tools (e.g., 'netlicensing_delete_license'), or warn about irreversible effects. The agent must infer usage from the name and context 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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