DTMFRAME

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description aligns ('Computation only'). It adds rich behavioral context: canonical_page carries complete price table including withheld listings, merchant links, affiliate disclosure; bounded compute class; links are user_navigation handoffs. No contradiction.

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?

Long but dense; every sentence adds value. Front-loaded with the purpose, then behavioral details. Not verbose; each clause conveys distinct information.

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?

Provides a partial output description (rows with canonical_page and site_entry_page) and key behavioral constraints, but lacks explanation of most parameters and the full response structure (since there's no output schema). Given the complexity (nested objects, 8 params), more would be expected.

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 0%, so the description must compensate. It only clarifies edition_ids ('2 to 10 edition ids') and indirectly hints at budget via 'cheapest known cost' and 'budget fit', but says nothing about detail, locale, region, constraints, ownership_claims, user_supplied_prices, or schema_version. Major gap for 8 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?

States a clear verb and resource: 'Computation only: per-edition differences... for 2 to 10 edition ids' – specific and scoped. It also references the channel policy shared with resolve_purchase_request, which hints at its niche without explicitly naming sibling tools.

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?

Implies usage: comparing 2–10 editions, and distinguishes from resolve_purchase_request via the shared channel policy (single vs. multiple). Also adds a usage warning: 'Links are user_navigation handoffs... not intended for prefetching or automatic clicking.' No explicit exclusions but enough to guide selection.

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?

Annotations already cover read-only/idempotent/destructive, and the description adds substantial behavior beyond them: prices are 'observations ... not guarantees'; withheld listings are marked withheld_by_channel_policy; links are user_navigation handoffs not for automatic clicking; canonical_page carries the complete table. No contradiction.

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 long but dense and front-loaded with the core return statement; each clause adds real semantic detail needed because there is no output schema. It sacrifices readability by packing many fields into a single prose block, so it's not a 5.

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?

Given the absent output schema, the description covers the main return fields, the optional lowest_observed shape, source withholding semantics, and link usage, which is enough for an agent to invoke the tool and interpret results. Minor omissions like condition semantics don't materially undercut completeness.

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?

With 0% schema description coverage, the description compensates by clarifying that product_id accepts a slug or edition_id and stating the region default/provenance. The optional condition parameter, however, is left entirely to the enum, so not all parameters receive the same treatment.

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?

States precise verb+resource: 'Returns the current merchant offers of one product' and enumerates identifying inputs. The scope (one product) and read-only nature distinguish it from dealing/search/compare siblings, even without naming them.

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?

Gives clear context for when it applies (current offers for a specific product; read-only, no purchase execution) and even warns that returned links are navigation handoffs not for prefetching. It does not explicitly name sibling alternatives or state when not to use it, so it stops short of a 5.

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?

The description adds substantial behavioral detail beyond the annotations: it lists the exact returned fields, states that no merchant links are returned, explains what canonical_page and site_entry_page contain, and warns that links are user_navigation handoffs not intended for prefetching or automatic clicking. It also confirms the read-only nature, consistent with readOnlyHint=true. This is exemplary transparency for a list endpoint.

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 long but every sentence carries useful information: purpose, ordering, output fields, filters, exclusions, page semantics, and link-handling warnings. It is front-loaded with the core purpose and then details. It could be slightly tightened, but the density is justified because there is no output schema to document the return shape.

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 read-only list tool with no output schema and seven parameters, the description is remarkably complete. It names all relevant output fields, explains the default state filter, clarifies the relationship to get_current_offers, describes the canonical and site entry pages, and warns about link handling. An agent has enough context to invoke the tool correctly and interpret the response.

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 86%, so the schema already documents most parameters. The description mentions the filters ('brand slug, category slug, kind, source, state (running by default), limit, cursor') but adds little beyond what the schema states; for example, 'state (running by default)' repeats the schema default. It does not explain cursor pagination or the slug normalization beyond the schema. This meets the baseline but does not exceed it.

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: 'Lists the sales and campaigns recorded by a Japanese music-production buying guide (region JP), running ones first'. It clearly identifies the tool's scope and differentiates it from get_current_offers by stating that per-product offers are handled there. This is far beyond a tautology and gives an agent a precise mental model.

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 gives clear context for when to call this tool: to list campaigns/deals with filters, running campaigns by default. It explicitly routes per-product offer lookups to get_current_offers ('offers of one product are returned by get_current_offers'). It does not explicitly contrast with search_products, compare_products, or resolve_purchase_request, but the use case is clear enough that an agent can select it appropriately.

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?

Annotations already mark read-only and idempotent; the description goes further with no purchase/persistence/notification, rate-limit sharing, partial-answer flags, and link handoff semantics. This is exactly the behavioral context needed beyond annotations.

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?

Dense, front-loaded 'Computation only' and no filler; the prose organizes inputs, outputs, and constraints in a logical order. It is long but every clause carries a distinct behavioral or semantic fact.

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 no output schema, it enumerates candidate fields, page-link semantics, partial results, and compute limits, which is strong coverage for a complex 11-param tool. It does not map each input mode to output kind, but that is a minor gap.

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?

With 0% schema coverage, the description must compensate, and it gives high-level meaning to constraints, free text, and ownership claims. However it does not explain mode, detail, locale/region, max_candidates, or user_supplied_prices, leaving an agent to infer those from names/enums.

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?

Clearly states the tool does computation-only candidate resolution from typed constraints and lists output components, so it is not a purchase or persistence action. It differentiates from sibling search/compare/list tools by emphasizing no purchase/persistence and candidate semantics. The verb 'resolve' plus the resource is specific.

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?

Provides a strong context cue ('Computation only', bound to one published snapshot, budget constraints) that tells an agent when this is the resolution engine rather than an external action. But it never names siblings or states explicit when-to-use/when-not-to-use conditions, so the agent must infer alternatives from sibling names.

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?

Beyond the readOnlyHint and idempotentHint annotations, the description discloses specific behavior: unresolved aliases return 0 products with unresolved_constraints=[alias_unresolved], canonical_page carries the full price table, and links are handoffs not meant for automatic clicking. This adds real operational context that plain annotations cannot 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 dense but every sentence adds operational value: scope, matching behavior, return fields, unresolved-alias behavior, page semantics, and link handling. The core search action is front-loaded, but the long canonical_page sentence could be split for easier parsing.

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?

Even though there is no output schema, the description specifies the returned fields, the unresolved-alias failure convention, the distinction between search results and current offers, and the meaning of returned page links. For a 9-parameter, read-only search tool with four enums, this is exceptionally 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 must compensate. It clarifies that brand accepts a slug or id, alias is normalized and can be an exact product name or registered alias, and that category, format, OS, and license are typed vocabulary. It does not explain limit or cursor semantics, though those are largely inferable from schema names, defaults, and constraints.

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 opening phrase 'Searches the product catalog...' names a specific verb and resource, then goes on to enumerate the exact filter dimensions (category, brand, product_id, alias, format, OS, license). It also distinguishes itself from get_current_offers by explicitly stating that current offers are returned by that tool.

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 clearly routes current-offer lookups to get_current_offers and warns that canonical_page links are user-navigation handoffs not intended for prefetching. It does not contrast with compare_products or resolve_purchase_request, but the primary usage boundary is well established.

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