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wake-word-training

by com.openwakeword

settle_x402_payment

Idempotent

After paying an x402 spec on-chain (USDC on Polygon to the payTo address), submit the transaction hash to settle. Needs >=3 confirmations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoWhat the token refers to. Default job.
tokenYesThe job_token or license_token being paid.
engineYesTarget engine. 'openwakeword': desktop / Raspberry Pi / Python (ONNX+TFLite, `pip install openwakeword`). 'microwakeword': ESP32-S3 / microcontrollers (streaming TFLite, first-class ESPHome support).
tx_hashYes0x-prefixed transaction hash

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already provide idempotentHint=true, destructiveHint=false, and readOnlyHint=false. The description adds crucial context: the need for >=3 confirmations and the connection to the x402 payment flow. No contradictions with annotations. This extra behavioral info is valuable beyond the structured fields.

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?

The description is two concise sentences, front-loaded with the main action and key conditions. No filler words. Every sentence adds unique value.

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 the input workflow and prerequisites thoroughly. However, it lacks any mention of the return value or what indicates success/failure. Given no output schema, a note on confirmation or response would improve completeness. Still, for a focused settlement tool with rich schema annotations, it is nearly complete.

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 100%, so the baseline is 3. The description adds contextual framing (e.g., 'USDC on Polygon' for token, 'submit the transaction hash' for tx_hash) but does not add new parameter semantics beyond what the schema already provides. It does not explain kind or engine further.

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's purpose: after an on-chain payment, submit the transaction hash to settle. It specifies the chain (Polygon), token (USDC), and required confirmations. This verb+resource+context is highly specific and distinguishes it from sibling tools like pay_commercial_license or create_training_job.

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?

The description explicitly tells when to use this tool: 'After paying an x402 spec on-chain... submit the transaction hash'. It also states a prerequisite ('Needs >=3 confirmations'), giving clear ordering relative to payment steps. No alternative or exclusion is needed; the workflow is implied by the sibling names.

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.

Resources