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Labs64

Labs64/NetLicensing-MCP

netlicensing_update_product_module

Modify a product module's settings: update name, activate/deactivate, set max checkout validity for floating models, adjust yellow/red thresholds for rentals, or change node secret mode for NodeLocked licenses.

Instructions

Update a product module's properties.

Args: module_number: Module to update name: New name (leave empty to keep current) active: Set active state (omit to keep current) max_checkout_validity: Maximum checkout validity in days (Floating model, omit to keep current) yellow_threshold: Remaining time volume for yellow warning (Rental model, omit to keep current) red_threshold: Remaining time volume for red warning (Rental model, omit to keep current) node_secret_mode: PREDEFINED or CLIENT (NodeLocked model, leave empty to keep current)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
module_numberYes
nameNo
activeNo
max_checkout_validityNo
yellow_thresholdNo
red_thresholdNo
node_secret_modeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description partially discloses behavior by noting what happens when parameters are omitted (e.g., 'leave empty to keep current'), but it does not explain side effects, auth requirements, or error conditions.

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 as a docstring with an initial purpose sentence followed by parameter explanations. It is concise without unnecessary fluff, though the parameter list is lengthy but necessary.

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

Completeness4/5

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

The description covers all 7 parameters with model-specific guidance. The output schema exists to handle return values, so its omission is acceptable. It provides sufficient context for agent invocation.

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?

Despite 0% schema description coverage, the description adds substantial meaning by explaining each parameter's purpose and model-specific context (e.g., 'Remaining time volume for yellow warning (Rental model)'). This goes beyond the schema's type-only information.

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 'Update a product module's properties', specifying the verb (update) and resource (product module). It distinguishes from create_product_module by name and purpose.

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?

No explicit when-to-use or alternatives are given. The agent can infer from sibling tools that this is for modifying existing modules, but no direct guidance is provided.

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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