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Labs64

Labs64/NetLicensing-MCP

netlicensing_list_payment_methods

Retrieve all configured payment methods for your NetLicensing account to manage billing and subscription options.

Instructions

List all payment methods configured for the account.

Args: filter: Optional server-side filter expression (e.g. 'active=true')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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 full burden. It states this is a list operation (implies read-only), but doesn't disclose authentication requirements, rate limits, pagination behavior, error conditions, or what 'configured for the account' means in practice. For a tool with zero annotation coverage, this leaves significant behavioral aspects undocumented.

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?

Two sentences with zero waste. The first sentence states the core purpose clearly. The second provides parameter guidance with a helpful example. The structure is front-loaded with the main functionality. Could potentially be improved with slightly more context about typical use cases.

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 there's an output schema (which handles return values), 1 parameter with good description coverage, and no annotations, the description is adequate but minimal. It covers the basic purpose and parameter semantics but lacks behavioral context (authentication, pagination, errors) and usage guidelines that would be helpful for an agent. For a simple list tool with output schema, this is the minimum viable.

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%, but the description provides meaningful context for the single parameter: it explains 'filter' is 'Optional server-side filter expression' and gives an example ('active=true'). This adds substantial value beyond the bare schema (which only shows type:string, default:""). Since there's only one parameter and the description covers it well, this earns a strong score.

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 'List all payment methods configured for the account' - a specific verb ('List') and resource ('payment methods') with scope ('for the account'). It distinguishes from siblings like 'netlicensing_get_payment_method' (singular) by emphasizing 'all' payment methods. However, it doesn't explicitly contrast with other list tools (e.g., list_tokens, list_transactions).

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?

No guidance on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., authentication requirements), when this would be appropriate versus other payment-related tools (like update_payment_method), or whether this should be used for administrative versus end-user scenarios. It's a basic statement of functionality without contextual guidance.

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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