Status of a library purchase. Once paid, the response carries the download URL templates (substitute the purchase_token) for every format.
| Name | Required | Description | Default |
|---|---|---|---|
| engine | Yes | Target engine. 'openwakeword': desktop / Raspberry Pi / Python (ONNX+TFLite, `pip install openwakeword`). 'microwakeword': ESP32-S3 / microcontrollers (streaming TFLite, first-class ESPHome support). | |
| purchase_token | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds that the response carries download URL templates, which is useful behavioral context, but it doesn't disclose details like whether the purchase must be fully paid or what happens if not paid. With annotations covering the read-only nature, a 3 is appropriate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the purpose and adds the key behavioral detail about download URL templates. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with 2 parameters and no output schema, so the description is mostly adequate. However, it doesn't explain what happens if the purchase isn't paid (e.g., error or pending status), and the 'purchase_token' parameter is undocumented. Given the simplicity, a 3 is fair.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50%: the 'engine' parameter is well-described with enum values and context, but 'purchase_token' has no description. The tool description doesn't add meaning for 'purchase_token' beyond the schema, so the gap remains. Baseline 3 is appropriate since the schema covers half the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: checking the status of a library purchase and retrieving download URL templates once paid. It distinguishes itself from sibling tools like buy_library_model and pay_commercial_license by focusing on status retrieval, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for checking purchase status and obtaining download URLs, but it doesn't explicitly state when to use this tool versus alternatives like get_commercial_license or get_training_job. It provides context about the response but lacks explicit when/when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.
