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Labs64/NetLicensing-MCP

netlicensing_create_product_module

Create a product module under a parent product with a specified licensing model, such as TryAndBuy, Subscription, or Rental, to define licensing terms and configurations.

Instructions

Create a product module with a licensing model.

Args: product_number: Parent product number: Unique module number (e.g. 'M01') name: Module name licensing_model: One of: TryAndBuy, Subscription, Rental, Floating, MultiFeature, PayPerUse, PricingTable, Quota, NodeLocked active: Whether the module is active max_checkout_validity: Maximum checkout validity in days (Floating model) yellow_threshold: Remaining time volume for yellow warning (Rental model) red_threshold: Remaining time volume for red warning (Rental model) node_secret_mode: PREDEFINED or CLIENT (NodeLocked model)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
product_numberYes
numberYes
nameYes
licensing_modelYes
activeNo
max_checkout_validityNo
yellow_thresholdNo
red_thresholdNo
node_secret_modeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are present, so the description must disclose behavioral traits. However, it focuses entirely on parameter explanations and does not mention side effects (e.g., creation is destructive), authentication needs, rate limits, or return value structure. The description lacks behavioral context beyond basic parameter defaults.

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 well-structured with a brief opening sentence followed by a bullet-like parameter list. It is concise for 9 parameters, though the Args section could be slightly tightened. Overall, every sentence earns its place.

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 complexity (9 parameters, model-specific behavior) and the presence of an output schema (so return values are not needed), the description covers parameter semantics thoroughly but omits behavioral context, usage guidelines, and error conditions. It is adequate but not comprehensive.

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

Parameters5/5

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

With 0% schema description coverage, the description fully compensates by explaining each parameter's purpose, valid values (e.g., explicit list of licensing models), and model-specific constraints (e.g., max_checkout_validity for Floating, thresholds for Rental, node_secret_mode for NodeLocked). This adds substantial meaning beyond the schema's bare titles and types.

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 tool's function: 'Create a product module with a licensing model.' This specifies the verb (create), the resource (product module), and a key distinguishing attribute (licensing model). It effectively differentiates from sibling create tools like netlicensing_create_license or netlicensing_create_product.

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, nor does it mention prerequisites or when not to use it. It only lists parameters, leaving the agent to infer usage context from the tool's name and siblings.

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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