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

Adds real behavioral context beyond the annotations: it returns a numeric chain and a counter-recommendation when the proposal is contradicted, and clarifies the three verdict states. 'Read-only' duplicates readOnlyHint=true, so that portion is redundant rather than additive.

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 dense sentences, front-loaded with the purpose and immediately followed by return values and the usage rule. The em-dash enumeration of proposal sources is slightly verbose but earns its place by clarifying scope.

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 not be exhaustively explained, yet the description still names the verdicts and the counter-recommendation. Combined with the pre-action usage rule, an agent has everything needed to call it correctly.

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 every parameter is already documented in the schema (sku, source, current_cpl, cpl_trend_days, proposed_action enum, competitor_active). The description adds no format, units, or edge-case guidance for any parameter, so 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 (test/audit) and resource (an advertising decision originating elsewhere), and enumerates the three possible verdicts returned. It implicitly separates itself from siblings like recommend_next_action by scoping to decisions this server did not produce.

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 an explicit trigger: 'Use before acting on any recommendation this server did not itself produce,' which also implies the exclusion (decisions the server generated). It does not name an alternative sibling tool by name, 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?

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered. The trailing 'Read-only.' merely restates the annotation, and the listed return fields duplicate the output schema, so the description adds little behavioral context beyond what structured fields provide.

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: the first states what is listed and what each entry contains, the second states when to use it. Nothing is wasted and the purpose is front-loaded.

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 of one optional filter parameter, the definition covers purpose, contents, and usage adequately, and an output schema exists so return structure need not be explained. Only pagination or result-size behavior is unaddressed, 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?

There is a single optional parameter, min_orders, whose schema description ('Only list products with at least this many orders') is complete at 100% coverage. The description does not reference this filter at all, so it adds no meaning beyond the schema; 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?

The description uses a specific verb+resource ('List the product codes present in the loaded snapshot') and even specifies the returned fields (order counts, price/cost availability). It implicitly routes the agent away from the measuring sibling by framing itself as the step 'before measuring', so an agent can distinguish it from measure_sku_funnel.

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 context cues: 'Use to resolve a product code before measuring, or to see what the dataset covers.' That covers the two main situations for calling it. It does not name a specific alternative sibling or state when not to use it, keeping it 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description reinforces 'Read-only' while adding real behavioral context: only mature/resolved cohorts are counted, null is returned with reliability 'insufficient' when the sample is too small, and results include sample size and a reliability flag. That is meaningful disclosure beyond the 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?

Front-loaded with the core action and scope, followed by usage guidance and return behavior; every sentence carries information. It runs slightly long, but nothing is redundant 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?

With an output schema present, the description need not enumerate return fields, and it still covers usage, cohort maturity, small-sample handling, and read-only status. Nothing an agent needs to call this correctly 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 coverage is 100%, so the baseline is 3, but the description adds conceptual meaning to the parameters by explaining that only cohorts 'old enough to have resolved' are counted, which clarifies the role of maturity_days and the window parameters beyond the raw ISO-date documentation.

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 (measure) and resource (call-centre approval, post-office buyout, and return rates for one product) with explicit scope (date window, resolved cohorts only). An agent can distinguish this from the sibling tools without opening any schema.

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 when-to-use guidance: 'before any decision that depends on approval or buyout rates, instead of assuming the portfolio-wide 65% approval and 52.5% buyout.' It does not name a sibling alternative, but the triggering condition is unambiguous.

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, and the description confirms 'Read-only.' Parity there. The description adds real behavior: it falls back to measuring the product itself when rates are omitted, and it reports drift vs. the current baseline. It does not disclose cost/latency of on-demand measurement, keeping it out of the 5.

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 sentences, each load-bearing: what is computed, the source substitution, the fallback, the trigger, and the flag. Verb+resource is front-loaded, and 'Read-only' is correctly placed last as a closing guarantee.

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-value explanations are unnecessary. For a 6-param read-only tool the description covers purpose, fallback behavior, and the business flag adequately. It omits when observed_* should be supplied vs. left null (beyond 'when omitted'), 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?

Schema description coverage is 100%, so each parameter is documented in the schema. The description adds only the fallback semantics ('measured on demand when omitted'), which is already stated per-param in the schema. It does not explain the observed_* vs. window_* interaction beyond that. 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 (recompute) and two named resources (Stop CPL, Goal CPL), defines each parenthetically in business terms, and distinguishes the computation source ('from its own measured funnel rates instead of portfolio-wide assumptions'). An agent can separate this from siblings like measure_sku_funnel.

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?

Explicit trigger: 'Use whenever a cost-per-lead target is being set, defended, or questioned.' Names the specific alert condition it surfaces (assumed target above true break-even, invisible in the ads dashboard). No sibling is named as an alternative, but the when-to-use clause is unambiguous.

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, destructiveHint=false and openWorldHint=false, so the closing 'Read-only' is redundant. However, the description adds real behavioral context beyond the annotations: the diagnosis taxonomy it applies, the signals it consumes, and the fallback behavior ('hold' with 'insufficient_data') instead of a forced recommendation. That fallback is the kind of trait an agent cannot infer from structured fields.

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 purpose in the first sentence, then layers the failure taxonomy, the usage trigger, and the fallback rule in a logical order. The 'call vs. post office' analogy consumes a clause that is illustrative rather than strictly load-bearing, and the trailing 'Read-only' duplicates the annotation, so it is dense but not maximally tight.

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 present, the description need not document return fields, and it correctly only hints at the shape ('action hold', 'diagnosis insufficient_data'). Given 8 parameters at full schema coverage plus annotations, nearly everything an agent needs to call this correctly is present; only explicit sibling routing is absent.

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% across all 8 parameters, so the schema already defines sku, current_cpl, window_*, cpl_trend_days, competitor_active and the creative signals. The description only gestures at them collectively ('measured funnel, its current cost per lead, and optional competitor and creative signals') without adding format, units, or interaction semantics. Baseline 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?

States a specific verb and resource ('Diagnose why a product is underperforming and return the matching remedy') and enumerates the exact failure modes it distinguishes (structural loss, contested auction, traffic quality, offer or price mismatch, creative fatigue, weak offer). An agent can tell this apart from measure_sku_funnel or audit_ad_verdict, which measure or judge rather than prescribe a remedy.

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 an explicit trigger ('Use when a campaign misses its target and the cause is not yet established') and a justification for the tool's existence (opposite remedies for leads that fail on the call vs. at the post office). It also states a boundary condition — returns 'hold'/'insufficient_data' rather than guessing on small samples. It stops short of naming sibling tools as alternatives, so it is clear context without explicit routing.

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