Agent Wholesale

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

With no annotations, the description carries the behavioral burden. It provides some useful context: 'Fetched at request time', 'Direct public HTTPS page retrieval with watched baseline evidence', and 'Reuse the same URL and field baselines to refresh the check incrementally'. It omits failure behavior, baseline initialization semantics, and any operational limits, so the transparency is only partial.

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 core sentence is front-loaded, but most of the description is filled with commercial and trust-related filler such as 'Canonical product ID', 'Price: $0.002 USD', and '72-hour live drift gate completed with 100% sentinel availability'. These details do not help an agent select or invoke the tool and push out the genuinely useful behavioral facts.

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 schema is simple and there is an output schema, but the description still fails to explain the core model: what 'watched changes' means, how fields map to page content, how baselines are initialized, and what a result looks like. It also provides no guidance for choosing this tool over its siblings, leaving the agent under-informed.

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 0%, so the description must compensate. It hints that 'url' is the public page and 'fields' are the watched baselines, but it never defines what a field is, what format it should take, how baselines are created, or how many are meaningful. This adds only minimal meaning beyond the bare parameter names.

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 opens with 'Check a public page for watched changes', which states a verb and resource and broadly distinguishes it from keyword-trends and web-search. However, 'watched changes' is left undefined, and the description does not explain what kinds of changes are detected or how the tool's output differs from a normal web search result.

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?

There is no explicit when-to-use or when-not-to-use guidance and no comparison to the sibling tools. The 'Repeat capability' line mentions reusing URL and field baselines, but it does not explain the initial setup workflow, prerequisites, or when this tool should be preferred over keyword-trends or web-search.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden of behavioral disclosure. It provides metadata about price, freshness, provenance, and reliability, but it does not describe the operational behavior an agent needs: what the comparison output contains, how the trend window works, what limits apply, or how failures surface. Generic reliability claims do not compensate for that gap.

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 compact and organized into scannable labeled metadata, with the core function stated first. Some metadata such as product ID and QA coverage is arguably marginal for an agent, but the text remains concise and does not obscure the purpose.

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?

For a tool with no annotations, zero schema descriptions, and two required parameters, the description is too thin. An agent does not learn how to format country or keywords, what the trend comparison window is, how this tool relates to siblings, or what a successful response looks like. Existing output schema helps with return values but not with input semantics or selection logic.

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 0%, so the description must compensate, but it only mentions 'keywords and country' in passing. It does not define acceptable country formats, keyword forms, or constraints such as the 5-item maximum, even though the schema itself provides no descriptions.

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 opens with a clear verb and object ('Compare keyword trend signals') and usefully clarifies the data source as normalized supplier trend data, not Google Trends. It does not, however, distinguish the tool from its siblings change-check and web-search, and the core phrase repeats the tool title.

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?

No explicit guidance is given on when to choose this tool over change-check or web-search. The only comparative statement, 'not Google Trends,' is a provenance clarification rather than a routing rule, and the repeat-capability note hints at refresh use cases without stating when to use or avoid the tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full burden. It discloses meaningful traits: freshness at request time, normalized organic results with source URLs, production endpoint reliability, and a cost of $0.01 per successful purchase. These go beyond what the schema or annotations would convey.

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 compact and front-loaded with its purpose before a set of short, labeled attributes. The metadata lines are mostly useful, though 'Canonical product ID' adds little for an AI agent selecting the tool.

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 covers purpose, freshness, provenance, reliability, cost, and repeat usage, and an output schema is present. Still, gaps remain around parameter semantics and explicit differentiation from sibling tools, so it is adequate but not fully complete.

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 0%, so the description should compensate, but it only mentions 'query, country, and limit' in passing as things to reuse or refine. It does not explain the expected format for country, the range or effect of limit, or any other parameter-specific detail.

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 opens with a specific verb and resource: 'Search the fresh public web'. It clearly identifies the tool's core function, though it does not explicitly contrast itself with the sibling tools change-check and keyword-trends.

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?

Usage is implied by the purpose and the repeat-capability note that says to 'Reuse or refine the query, country, and limit for follow-up searches.' However, there is no explicit guidance about when to choose this tool over change-check or keyword-trends, nor any exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.