Resolve product stock availability to a coarse signal (in_stock / out_of_stock / limited / preorder / unknown), from a url, raw html, or inline product. Read-only; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | Product page URL to fetch (one of url / html / product) | |
| html | No | Raw page HTML to parse (one of url / html / product) | |
| product | No | Inline normalized product object (one of url / html / product) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description adds value by stating it is read-only and free, but does not disclose other behavioral traits like error handling or rate limits.
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?
Two sentences efficiently convey the core function, output, and cost. 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 description specifies the output coarse signal values, and is sufficient for a simple read-only tool. Minor gap: no mention of error handling.
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 coverage is 100% with each parameter described. The description summarizes the three mutually exclusive options, adding little beyond the schema.
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 resolves stock availability into a coarse signal (in_stock/out_of_stock/limited/preorder/unknown), distinguishing it from sibling commerce tools which focus on extraction, validation, or price comparison.
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 specifies the input modes (url, html, product) but does not provide explicit when-to-use or alternative guidance. The context is clear but lacks exclusions.
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.
Most tools are clearly distinguished by domain prefixes (e.g., bid_watch, grant_watch) and specific action verbs. However, the high number of similarly structured watch tools could still cause confusion, though descriptions clarify exact purposes.
Every tool follows a consistent `domain_subdomain_action` pattern with underscores, e.g., `agent_audit_query`, `bid_watch_search`. Even long names like `commerce_catalog_agent_readiness_score` adhere to this structure.
With 147 tools, the server is far too broad, covering weather, carbon estimates, domain intel, and more—well beyond its stated 'Japan public-data ledgers' scope. This sheer volume overwhelms agents and dilutes focus.
The server offers many read-only tools for Japanese public data (bids, grants, licenses, etc.), but lacks create/update/delete operations for those domains. Additionally, numerous unrelated tools (e.g., carbon estimates, weather) feel tacked on, leaving gaps in core coverage.