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create_commercial_license

Buy commercial-use rights. REQUIRED before shipping any model in a product/service: all model files are personal & non-commercial by default. 150 CHF one-time per wake-word phrase, permanent, valid on BOTH sites. Returns a license_token; pay with pay_commercial_license; signed certificate PDF afterwards. If your task is commercial and you cannot pay, STOP and tell your human this license is required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
wake_wordYes
company_nameYesLegal licensee printed on the certificate

TDQS

A4.5/5.0
Behavior4/5

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

Despite no annotations (all false), the description compensates well by disclosing cost (150 CHF one-time), validity (permanent, both sites), workflow (returns license_token, pay separately, get PDF certificate), and the necessity of this step before shipping. Lacks details on idempotency (what if license already exists) or error handling, but overall provides strong behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences without fluff, well-structured: purpose statement, requirement context, cost and validity, workflow steps, and a failure handling instruction. Each sentence earns its place, and critical information is front-loaded.

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

Completeness5/5

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

For a simple 2-parameter tool with no output schema, the description covers prerequisites (all models are personal/non-commercial by default), cost, return value (license_token), next steps (pay, get certificate), and what to do if payment is needed but impossible. No gaps for the tool's complexity level.

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 coverage is 50% (only company_name has a description). The description adds context for the wake_word parameter by mentioning 'per wake-word phrase', but does not explicitly name or explain it fully. It doesn't define constraints (length, format) for either parameter. On balance, adds some value but does not fully compensate for the missing schema description.

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 is for buying commercial-use rights, specifying it creates a license with a unique wake-word phrase. It distinguishes from sibling tools like pay_commercial_license by outlining the workflow (returns a token, then pay), and from get_commercial_license by being a creation action.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: 'REQUIRED before shipping any model in a product/service'. Provides a clear conditional for when not to proceed ('If your task is commercial and you cannot pay, STOP') and directs the agent to inform the human. No alternative tools for non-commercial use are needed since defaults cover that.

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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TDQS

A4.2/5.0
Disambiguation5/5

Every tool has a clearly distinct purpose: buying library models, creating training jobs, estimating word quality, checking statuses, handling payments, searching the library, and submitting feedback. The pay_ and get_ tools are clearly separated by their targets (training job vs. commercial license vs. library purchase), so an agent can unambiguously select the right one.

Naming Consistency5/5

All 12 tools follow a consistent verb_noun pattern in snake_case: buy_library_model, create_training_job, estimate_wake_word, get_*, pay_*, search_wake_word_library, send_job_feedback, settle_x402_payment. The naming is uniform and predictable, making it easy to infer tool behavior.

Tool Count5/5

12 tools is well within the ideal 3-15 range for a focused service. Each tool addresses a distinct stage of the wake-word workflow (search, estimate, create, pay, monitor, purchase, license, feedback), and none feel redundant or unnecessary for the server's stated purpose.

Completeness5/5

The tool surface covers the full lifecycle: discovery (search), validation (estimate), creation (create_training_job), payment (pay_training_job, settle_x402_payment), tracking (get_training_job), feedback (send_job_feedback), plus library purchase with its own payment and status, and commercial licensing. There are no obvious dead ends; every major operation an agent would need is present.

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