Validate product-feed completeness: which required fields are present or missing and a completeness score, from a url, raw html, or an inline product object. 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 provided, the description carries full burden. It states 'Read-only; price 0.0 (free)', which adds transparency about side effects and cost. However, it lacks details about response format, limits, or what happens with invalid inputs.
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 two sentences: first delivers purpose and output, second states read-only and cost. No fluff, front-loaded, and efficient for an agent to parse.
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?
Despite parameter coverage, the description lacks details about the output (completeness score, required fields), error handling, or how the input sources differ. For a validation tool in a large catalog, agents would benefit from more context on return values and behavior.
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 100%, so the schema already documents the three parameters. The description briefly mentions 'url, raw html, or an inline product object' but adds no additional meaning beyond the schema's descriptions. Baseline score of 3 is appropriate.
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 validates product-feed completeness, listing required fields, completeness score, and three input sources (url, html, product). This specific verb+resource combination distinguishes it from sibling tools like product_extract or availability_check.
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 validating completeness and specifies input options but does not explicitly guide when to use this tool over alternatives like commerce_catalog_product_extract or commerce_catalog_availability_check. No exclusions or when-not advice.
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.