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Labs64

Labs64/NetLicensing-MCP

netlicensing_list_licensees

Retrieve and filter customer records for a specific product in NetLicensing to manage software licensing data.

Instructions

List all customers (licensees) for a product.

Args: product_number: Product to list customers for filter: Optional server-side filter expression (e.g. 'active=true')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
product_numberYes
filterNo

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 the full burden of behavioral disclosure. It states this is a list operation, implying it's likely read-only and non-destructive, but doesn't confirm this or mention other traits like pagination, rate limits, authentication needs, or error handling. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by a brief parameter section. There's no wasted text, and the structure is clear. However, the parameter section could be more integrated into the flow rather than a separate 'Args:' block, slightly affecting readability.

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 moderate complexity (2 parameters, list operation), no annotations, and an output schema (which reduces the need to describe return values), the description is minimally adequate. It covers the basic purpose and parameters but lacks behavioral context, usage guidelines, and deeper parameter details. With the output schema handling return values, it's complete enough for basic use but not robust.

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

Parameters3/5

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

Schema description coverage is 0%, so the schema provides no parameter descriptions. The description adds basic semantics: 'product_number: Product to list customers for' and 'filter: Optional server-side filter expression (e.g., 'active=true')'. This clarifies the purpose of each parameter and provides a filter example, but doesn't detail format constraints, allowed values, or how filtering works beyond the example. It partially compensates for the schema gap.

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 tool's purpose: 'List all customers (licensees) for a product.' It specifies the verb ('List'), resource ('customers/licensees'), and scope ('for a product'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'netlicensing_get_licensee' or 'netlicensing_list_products', which could cause confusion about when to use this versus other list operations.

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. With many sibling tools (e.g., 'netlicensing_get_licensee' for a single licensee, 'netlicensing_list_products' for listing products), there's no indication of prerequisites, typical use cases, or exclusions. The agent must infer usage from the name and parameters 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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