Peak Answer

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

Annotations already declare readOnlyHint, openWorldHint and idempotentHint, so the safety profile is covered. The description adds genuinely new behavioral context: the call is fast ('Returns in seconds') and the optional checks are the slow path. It does not say anything about auth or rate limits, but the latency disclosure is real added value.

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 sentences, front-loaded with the core purpose and single-call scope, then the invocation triggers, then the optional-cost caveat. The long enumeration of checks is dense but each item earns its place by telling the agent what the report contains.

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?

There is no output schema, but the enumerated check list effectively describes the report contents, and the latency note sets expectations for turnaround. An agent has enough to call it correctly; only the exact return shape and any error/auth behavior are unaddressed.

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 both parameters are already documented, and the schema itself already states that each extra check adds 10 to 30 seconds. The description's 'Add extra_checks for the slower ones' therefore largely restates the schema, leaving it at the baseline 3 with no additional syntax or format 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?

States a specific verb and resource ('full technical and answer-readiness audit for one URL, in a single call') and enumerates the exact check set, so an agent knows precisely what comes back. It does not explicitly differentiate itself from the sibling geo_audit, which is the one plausible overlap, so it stops 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?

Gives concrete trigger phrases ('audit my site', 'check my site', 'why am I not being recommended') that map user intent onto the tool. It is clear about when to use it but offers no when-not or explicit alternative, so no exclusion guidance.

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 openWorldHint=true, so the safety profile is covered. The description adds only the 'ready to ship' framing and the per-call constraint; it never explains that output is returned as text rather than persisted, which is the non-obvious trait for a 'generate' tool.

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 the artifact list front-loaded and the usage constraint last. Nearly every clause carries information; the enum values are echoed, which is the only mild redundancy.

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 only two required params, full schema coverage and annotations covering behavior, the description is sufficient to call the tool. It stops short of explaining output format or size/limits for the generated artifacts, which the absence of an output schema leaves unaddressed.

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 both parameters are already documented in the schema, including the mapping of input type to artifact. The description's restatement ('llms_txt and faq_schema for a site ... for a topic') is largely redundant with the schema's own input description, 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?

The description states a concrete verb ('Produce') and enumerates the exact artifacts the tool emits (llms_txt, faq_schema, content_brief, prompt_set, semantic_keywords), which lets an agent match the enum to real outputs. It does not explicitly differentiate itself from the sibling tools, but the read-oriented siblings (audit_site, my_visibility) are clearly a different category.

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?

'One call per thing' is a genuine invocation guideline that tells the agent not to batch multiple artifacts into a single call. However, there is no guidance on when to prefer this tool over the sibling audits, no prerequisites, and no exclusions.

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 annotations (readOnlyHint/idempotentHint/openWorldHint), the description discloses the key behavioral traits an agent needs: this is a slow, asynchronous background run that must be re-invoked with the same domain to retrieve results, and it takes a few minutes. That poll-and-collect contract is exactly the kind of context annotations cannot express.

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 what the tool does, then latency/async behavior, then the sibling alternative. Nothing is wasted for a tool of this complexity. The first sentence leans slightly on marketing phrasing, which slightly softens the otherwise tight structure.

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?

There is no output schema, so the description carries the burden of explaining results; it sketches the return ('shows you who gets named') without detailing the report structure. Combined with the async polling guidance and the alternative-tool pointer, an agent has enough to invoke and collect correctly, though the result shape remains somewhat abstract.

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 single 'url' parameter is already documented as 'The domain to audit,' so the baseline is 3. The description adds meaningful semantics by stating you call it again with the same domain to collect the result, clarifying that the argument doubles as a stable run key rather than just a one-shot input.

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 states what the audit produces: 10 real buying questions about the user's category and who gets named in the answers. It reads somewhat like product copy ('Peak Answer', 'who gets named') rather than a crisp verb+resource, but an agent can still grasp the output. It also distinguishes itself from the sibling audit_site, which helps selection.

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 explicitly routes the agent: 'Use audit_site for anything that needs an answer now,' giving a clear condition that selects the alternative. It also explains the invocation pattern (call again with the same domain to collect the result). No hard exclusions beyond the timing tradeoff, but the routing guidance is concrete.

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 (readOnlyHint, idempotentHint, openWorldHint=false), so the description's job is to add context, and it does by disclosing the returned fields: reason, expected effect, and targeted engine. It omits freshness/recency, result limits, and whether an empty list means 'no recommendations' or 'not yet analyzed'.

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 sentences, no filler, and the content of the response is front-loaded before the usage cue. Every clause earns its place.

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 no input parameters and no output schema, the description must carry the return-value burden, and it names the three fields the agent will see. Adequate for a simple read tool, though it leaves edge cases (empty results, staleness) unaddressed.

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 tool takes zero parameters, so there is nothing for the schema to document and no description-side compensation is required. Baseline for a parameterless tool 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 resource ('everything Peak Answer currently recommends for the signed-in brand') plus the payload shape (reason, expected effect, target engine), which clearly separates it from data-oriented siblings like my_visibility or my_questions. It never names a sibling explicitly, so differentiation is implied rather than stated.

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?

'Use this when the user asks what to do next' gives a concrete triggering condition, which is more than most tools in this set offer. There is no guidance on when NOT to use it or which sibling to prefer for adjacent questions (e.g. raw visibility data vs recommendations).

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, idempotentHint, and openWorldHint=false, so the safety profile is covered. The description adds the non-obvious sequencing behavior — that this is the entry-point lookup that resolves 'my site' ambiguity — which annotations cannot express. It stops short of any format/pagination detail, but the payload shape is already enumerated.

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 sentences, no filler, and the payload enumeration comes before the routing rule. Slightly dense, but every clause carries information — nothing restates the name or title.

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 zero-parameter lookup with no output schema, the description compensates by enumerating the returned fields (domain, name, plan, question count, integrations) and by explaining when to call it. Nothing an agent needs to invoke it 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?

Zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description correctly implies the call is parameterless by framing the result as scoped to 'this connection'.

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 exactly what the tool returns — the connection's brand: domain, name, plan, tracked question count, and connected integrations — which is a specific resource with an enumerated payload. It is clearly distinguishable from the my_* siblings (my_questions, my_visibility, my_search_console), which each cover a narrower slice.

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 routing instruction: 'Call this first whenever the user says my site, my website or my domain, rather than asking them which site they mean.' It states both the trigger phrase and the behavior to avoid, which is exactly what an agent needs for tool selection among many my_* tools.

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, closed-world), so the description only needs to add operational context. It does: a 90-day retention window, per-engine granularity, and that raw model answers are included — meaningful disclosure about what the response contains.

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 front-loads what the data is, the second states the invocation condition. No filler or redundancy.

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 no output schema, the description carries the burden of describing the return and does so (time window, engine breakdown, model answers). It omits pagination/result-size behavior, but that is a minor gap for a single-question history lookup.

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 single parameter is fully documented ("question text, or enough of it to match"). The description's "by name" phrasing hints at fuzzy matching but adds nothing the schema doesn't already say, 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?

The description states a precise resource and scope: 90-day, per-engine history for one tracked question, and explicitly says it includes model responses. That is enough to separate it from the list-oriented sibling my_questions, though it never names a sibling directly.

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?

"Use this when the user asks about one specific question by name" gives a concrete trigger condition that routes the agent correctly. It stops short of stating when NOT to use it or naming my_questions as the alternative for browsing questions.

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 openWorldHint=false, so safety and idempotency are covered structurally. The description adds useful scope and payload context — results are scoped to the signed-in brand and report competitor ownership and answer volatility. However, it says nothing about result size, ordering, or pagination, so it goes only modestly 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?

Two sentences, front-loaded with what the tool returns and followed by the usage trigger. No filler, no repetition of the title or annotations.

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 single-optional-parameter read tool with full annotations and no output schema, the description adequately covers scope and returned fields. It is slightly thin on volume/ordering behavior and on how it relates to my_question_history, which an agent might need to choose correctly between the two.

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 one optional boolean parameter and schema description coverage is 100%, so the schema fully explains only_losing. The description's phrase 'what they are losing' loosely hints at that filter but adds no syntax or behavioral detail beyond the schema; 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 resource (every buying question tracked for the signed-in brand) plus the three things returned: whether the brand is named, who wins instead, and how volatile the answer is. It is clearly distinguishable from generic siblings like my_brand or my_visibility, though it never contrasts itself with the closely related my_question_history.

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 the user asks what they are losing or which questions are worth attacking. That is a clear context, but there are no exclusions or named alternatives (e.g., when to prefer my_question_history or my_actions instead), so it stops short of full routing guidance.

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 readOnly, idempotent, and closed-world, so the safety profile is covered. The description adds genuinely useful behavioral context beyond that: the ranking window (5–15) and the fact that results are scoped to the signed-in user's connected Search Console, explaining why no parameters are needed.

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 sentences, no filler, with the result definition front-loaded and the invocation guidance second. Every clause earns its place.

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 no params, no output schema, and annotations covering safety, the description supplies what an agent needs: what is returned and when to reach for it. It could still note the list/ranking ordering of the result, but nothing essential 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?

The tool takes zero parameters, so the baseline is 4. The description implicitly explains the parameterless design by stating the brand scope is the signed-in user's connected Search Console rather than something passed in.

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 exact resource (Google queries for the signed-in brand from connected Search Console) and the precise filter (positions 5–15), which is far more specific than the title alone. It does not explicitly differentiate itself from plausible siblings like my_visibility or my_questions, so it stops 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 concrete when-to-use triggers: 'quick SEO wins' or 'what is close to page one.' However it names no alternative tool or exclusion condition, so an agent has no explicit routing rule when this overlaps with my_visibility.

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 readOnly, idempotent, and closed-world, so the safety profile is covered. The description adds meaningful scope beyond that: the result is scoped to the signed-in brand and includes trend/movement data, which tells the agent what kind of read this is.

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 sentences with no redundancy: the first states the payload, the second states the trigger conditions. Content is front-loaded with the returned data rather than preamble.

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 no input parameters and no output schema, the description is the only source describing what comes back, and it does so at a useful level (index, per-engine presence, movement). A little more on freshness/period or comparison basis would fully close the gap, but it is largely 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?

The tool takes zero parameters, so there is no parameter semantics burden to carry; the baseline of 4 applies. Nothing in the description misleads about inputs.

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?

Names a specific resource (the signed-in brand's visibility index) and enumerates what the response contains: per-engine presence and movement over time. This is clearly distinguishable from siblings like my_brand or my_questions, which cover brand identity and question lists respectively.

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 explicit triggering conditions: 'when the user asks how they are doing, whether anything changed, or how they compare to their category.' That is solid when-to-use guidance, though it never names an alternative sibling tool for the comparison case, which other tools in this server may cover.

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