Endurance Wire

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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), but the description adds genuinely useful behavior: unresolved values are returned as null and never substituted with generation ranges, and buy_links are included. That null-handling contract is non-obvious and valuable, though return-shape detail is partly covered by the output schema.

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?

Four tight sentences, front-loaded with the operation and return content, then the parameter-driven behavior and the null contract. No filler, and each sentence carries 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?

With annotations supplying the safety profile and an output schema documenting the return structure, the description covers the remaining semantic gaps (size selection behavior, null semantics, buy_links). It omits any guidance on when another sibling tool is the better choice, which is the main remaining 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?

Schema coverage is 100%, so the baseline is 3, but the description adds meaning the schema does not: selecting the size params produces size-specific specs while omitting them yields generation summaries, and two sizes of the same model are supported. This clarifies the semantic effect of the optional parameters rather than restating them.

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 'Spec diff between two products from a comparison table' names a specific operation (diff) and resource (two products), and the scope is clear enough to separate it from a single-product lookup. It does not explicitly name or contrast with siblings like get_product, so it falls short of a 5.

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?

It gives clear conditional guidance: supply a_size_mm/b_size_mm for size-specific specs, omit them for generation summaries, and it notes the same-model-two-sizes case. There are no explicit when-not-to-use rules or sibling alternatives (e.g., get_product for a single product), so it stops at clear context without routing.

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 the safety profile (readOnly, idempotent, non-destructive). The description adds genuinely extra context: the prediction methodology (historical cadence averages) and that results carry overdue flags, which matters for interpreting the output. It stops short of explaining uncertainty, coverage, or empty-result behavior.

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 tight sentences with no filler; the scope (per flagship family) and the calculation basis are front-loaded. Every clause carries 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?

With annotations covering safety, an output schema covering the return shape, and a fully documented parameter, the description supplies enough for correct invocation and interpretation of the forecast basis. Minor gap: no statement of what happens when 'family' is omitted or what an overdue flag signifies.

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?

The single 'family' parameter has 100% schema description coverage, including the full enumerated value list, so the schema carries the burden. The description adds nothing about the parameter, such as whether omitting it returns all families, so the baseline 3 applies.

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 specific verb and resource: predicted next-release windows per flagship family, with the derivation basis (average historical release cadence). An agent can distinguish this from get_releases (past data), but the description never names or contrasts the siblings it most overlaps with, notably get_rumors (upcoming-product speculation).

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: read-only, derived forecasts about future releases. The phrase 'computed from average historical release cadence' indirectly signals this is data-driven rather than rumor-based, but there is no explicit when-to-use/when-not guidance and no direction to get_rumors or get_releases for the alternative question.

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 establish read-only, idempotent, non-destructive behavior, so the safety profile is covered. The description adds genuine domain context beyond them: what the payload contains (generation chain, size_variants with per-model caveats, buy_links) and two interpretation rules ('Missing size data is unknown', 'Do not apply generation-level battery ranges to individual sizes').

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?

Front-loads the search behavior, then lists the returned fields, then the two caveats. Dense but each clause earns its place; the caveat sentences are the most valuable part and are not padding.

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?

An output schema exists so return-structure explanation is not strictly required, yet the description still flags the analytical pitfalls (unknown sizes, generation-vs-size battery ranges) an agent would otherwise get wrong. Only the missing sibling routing keeps it from being 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 coverage is 100% and the sole parameter is documented as 'Full or partial product name'. The description reinforces partial-match semantics and supplies examples, adding modest value 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?

States a specific verb and resource ('Find products by (partial) name') and grounds it with concrete examples ('fenix 9', 'forerunner 970'). It implies a different scope than siblings like get_releases or compare_generations, but never names them, so differentiation is left to inference.

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 partial-name matching semantics imply when this tool is appropriate, and the interpretation caveats help after the call. But there is no explicit when-to-use/when-not guidance and no routing against compare_generations, get_releases, or get_predictions.

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 declare this a read-only, idempotent, non-destructive, closed-world operation, so the safety profile is covered. The description adds one genuinely useful behavioral note — that unknown sizes are explicitly marked — but otherwise restates return content that the output schema already defines.

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?

Three short sentences, front-loaded with the core verb and ordering, then filters, then payload. The third sentence partially duplicates the output schema, which keeps it from being perfectly lean, but nothing is bloated.

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?

With an output schema documenting the return shape and annotations covering the safety profile, the description only needs to establish purpose, ordering, and filterability — which it does. The only minor gap is the absence of guidance on alternatives.

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 all four parameters (line, brand, limit, since) are already documented with enums and formats. The description only echoes brand, product line, and date filtering without adding syntax or defaults, so baseline 3 is correct.

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 specific verb and resource ('List product releases') plus ordering ('newest first'), so the agent immediately knows the operation. It does not explicitly distinguish itself from siblings like get_product or compare_generations, but the scope (a list of releases) is clear enough to disambiguate in practice.

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 says what can be filtered ('by brand, product line, or date'), which implies usage, but never states when to choose this tool over get_product or compare_generations, nor any exclusions or prerequisites. Adequate but leaves routing to inference.

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 declare readOnlyHint=true, idempotentHint=true and destructiveHint=false, so the safety profile is fully covered without the description. The description adds that results carry confidence status and sources, which is useful behavioral context about the payload. It does not discuss freshness, caching, or volume of results, so it earns a solid but not high score.

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?

One sentence, no filler, with confidence status and sources front-loaded. It is a noun fragment rather than a verb-led statement, which slightly weakens the opening, but nothing is wasted.

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?

An output schema exists, so return values need no explanation, and annotations cover the safety profile. For a zero-required-parameter read-only lookup, the description says enough to invoke it correctly; only alternative-routing detail is missing.

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% for the single 'status' parameter, and the schema enumerates the four allowed values, so the description need not restate them. The description never mentions status filtering, but the baseline for fully documented params is 3.

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 names the specific resource and scope: current rumors, leaks and confirmed items, plus the confidence status and sources attached to them. That is materially clearer than the bare name 'get_rumors' and tells an agent what corpus it returns. It stops short of distinguishing itself from siblings like get_releases or get_predictions, which overlap in the same news/speculation space.

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 when-to-use, when-not-to-use, or alternative routing guidance. Given siblings such as get_releases and get_predictions, an agent has to guess whether unconfirmed items belong here or with releases. Usage is only inferred from the noun phrase describing the content.

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