found-by-ai-monitor

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

The description adds meaningful behavioral context beyond the readOnlyHint annotation: the read is refreshed daily, results fall into full/partial/blank categories, and a character count is included. It also explains the real-world implication of a blank rendering, which helps the agent interpret results. No contradiction with 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?

The description is tightly written in three sentences, with the core result front-loaded. Each sentence earns its place: what the read returns, why that matters, and how this tool is unique among siblings. There is no redundant restating of the name or schema.

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 read-only tool with no output schema, the description provides enough context for an agent to call it and interpret the result. It names the possible render states, the character-count metric, the refresh cadence, and the significance of a blank render. 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 has zero parameters, so the schema carries no semantic burden. The description correctly implies that the tool operates on the current site itself. With no parameters, the baseline of 4 is appropriate; no additional parameter detail is needed.

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 clearly states what the tool does: it performs a headless agent-browser read and reports whether the site renders full, partial, or blank, including character counts. It also distinguishes itself from siblings by noting that no other tool in this lane measures this, so an agent can identify it without ambiguity.

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 makes the unique use case explicit: measuring how visible a site is to agentic AI. It doesn't name specific alternative tools or when not to use it, but the statement 'No other tool in this lane measures it' gives clear contextual guidance that this is the tool for agent-render 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 mark the tool readOnlyHint=true, and the description adds behavior-relevant context: data comes from the site's own beacon, is limited to 30 days, and includes tracking wiring status so the agent knows the data may reflect installation state. It does not overpromise or imply side effects.

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 well-organized sentences deliver the core scope first, then the higher-level purpose. Every phrase earns its place, and the 'closes the loop' framing adds context without 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?

For a no-parameter, read-only statistics tool, the description is largely complete: it names the time window, aggregation dimensions, source mechanism, and tracking status. It does not enumerate possible values of tracking status or edge cases like no beacon data, but the core calling decision is fully supported.

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 are zero parameters, so the description carries no parameter burden. It still adds value by clarifying what data the tool returns (totals by engine/week and tracking status), which would be the only meaningful semantic content for a no-input tool.

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 specific verb-resource relationship: it returns AI-sent visitors to the site over a fixed 30-day window, broken down by engine and week. It also distinguishes itself from siblings like get_mentions or get_visibility by emphasizing 'AI engines actually sent' and 'recorded by the site's own beacon.'

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 usage context: use this tool when you need the direct traffic driven by AI engines, with attribution to engine and week. It does not explicitly name alternative sibling tools or state when not to use it, but the focus on 'visitors the AI engines sent' and 'tracking wiring status' makes the intended use case unmistakable.

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 only declare readOnlyHint=true, so the description carries the rest — and it delivers: answers are trimmed to `chars`, flags (Named/Cited/Listed/Found via search) are defined and noted as overlapping, it warns that older flags may not distinguish these types and that the retained answer should be read, and it clarifies that 'Unavailable is not a negative result.' These interpretation caveats are exactly the behavioral context annotations cannot supply.

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 output is front-loaded in the first sentence, and the flag-definition sentences each earn their place by preventing misinterpretation. The opening sentence is an extremely dense run-on list, and the flag glossary could be tightened, but there is 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 must convey the return shape, and it enumerates the fields well (engine, model, date, named count, search-vs-memory, competitors, fan-out searches, demand). What it omits is pagination mechanics — the `offset` parameter references `nextOffset`, a response field the description never explains. That single gap keeps it from a 5.

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 the baseline is 3. The description only restates parameters already documented ('Filter by a question substring or an engine,' 'trimmed to `chars` characters') and adds no format, default, or boundary detail beyond the schema. It does not compensate further because it does not need to.

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 verb+resource: it returns 'the exact answer each engine gave to each tracked buyer question in the latest deep measurement,' and enumerates the accompanying fields (engine, model, date, named/cited flags, competitors, fan-out searches, demand). This is specific enough that an agent can distinguish it from siblings like get_mentions or get_citation_sources, which aggregate rather than return verbatim per-question answers.

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 says 'Filter by a question substring or an engine,' which is parameter guidance rather than when-to-use guidance, and it frames the tool as 'the receipt behind every score' — an implicit cue. However, it never states when to prefer this over the sibling aggregation tools (get_mentions, get_citation_sources, get_share_of_voice) or any preconditions. Usage is implied but never explicitly routed.

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 readOnlyHint annotation already covers the safety profile, and the description adds useful behavioral context by explaining that the result is based on a real corpus, not an estimate. It also mentions the distribution above and below, which is a behavior of the response. This is adequate but does not go into deeper detail about data freshness or category definitions.

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 two sentences with no unnecessary information. The main output is stated upfront, and the clarifying 'Real corpus, not an estimate' is short and value-adding. Every sentence 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?

This is a simple read-only tool with no input parameters, so the description is mostly sufficient for an agent to select and invoke it. It explains what the tool returns and the nature of the data. The main gap is the lack of a more precise definition of 'measured category' and the exact shape of the distribution data, but the low complexity keeps the omission minor.

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 has zero parameters and the schema is effectively empty, so there are no parameter semantics for the description to clarify. The description compensates by explaining what the output means, which helps an agent interpret the returned rank, percentile, and distribution. This meets the baseline for a no-parameter tool.

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 clearly states what the tool does: it positions the current business against all other measured businesses in its category and identifies the exact outputs (rank, percentile, and distribution above/below). The phrase 'Real corpus, not an estimate' further clarifies the data source. This distinguishes it from other sibling get_* tools that focus on traffic, mentions, or share of voice.

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 implies when to use it: whenever an agent needs a category benchmark based on actual measured data rather than estimates. However, it does not explicitly state when-not-to-use it or name alternatives such as get_rivals or get_share_of_voice. The usage context is clear but not differentiated from siblings.

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?

Read-only behavior is already covered by readOnlyHint=true, and the description adds valuable behavioral details: ordering is by most-cited first, the tool indicates whether this business appears on each domain, and it reports actual AI citations. This goes beyond the annotation without contradicting it.

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 tightly written sentences deliver the core output, ordering, and the business-appearance flag, then end with an actionable insight. There is no filler or redundant repetition of the schema.

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 parameters and a read-only annotation, the description covers the essential return information: a ranked list of domains with a business-presence indicator. It is complete for an agent to know what the tool will produce and why it matters.

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 has zero parameters, so the baseline is 4. The description does not need to explain parameter meaning, and it correctly frames the implicit scope as 'answers matching this category.'

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 clearly states the tool returns the domains AI engines cite in answers for a category, ranked most-cited first, and indicates whether the business appears on each. This distinguishes it from siblings like get_cited_queries (queries vs domains) and get_mentions (mentions vs citation sources).

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 strategic context: use this to find where being mentioned matters more than on-site changes. It does not explicitly name alternatives or exclusions, but the purpose is sufficiently distinct from sibling 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?

Beyond the readOnlyHint annotation, the description discloses key behavioral traits: scope is limited to previously run measurements, results include which engines named the competitor, frequency, last-seen timing, and co-named competitors, and it also reports domain citations. The coverage caveat is especially valuable for correct interpretation of absence.

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 efficiently written and front-loads the core direction-first framing ('Reverse lookup over every measurement we have already run'). The first sentence is long and enumerative, but it earns its length by compensating for the missing output schema.

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 enumerates the result contents clearly: buyer questions, engines, frequency, last seen, co-named parties, and citation sources. Combined with complete parameter schema documentation and the important coverage caveat, an agent has enough context to invoke and interpret the tool 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 coverage is 100%, so all four parameters are already documented in the schema. The description adds interpretive context about coverage and result meaning but does not provide substantial new parameter-level semantics beyond what the schema already gives.

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 specific verb and resource: 'Reverse lookup' over measured buyer questions, returning where a competitor was named or cited. It clearly distinguishes this from forward source lookups by framing it as the inverse direction, and the coverage caveat further clarifies exactly what the tool does.

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 use this tool: when you have a competitor's brand name or domain and need the buyer questions that named it. It also provides interpretive guidance about thin results meaning unmeasured questions, not competitor absence, though it does not explicitly name sibling alternatives or when not to use it.

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 readOnlyHint annotation already establishes that this is a safe read operation. The description adds useful context by listing the document's contents and noting it matches context.md, but it does not describe response format, freshness, or potential caveats. No contradiction with 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 concise sentences: the first front-loads the document scope and contents, the second gives a concrete reference point via context.md. Every word earns its place with no repetition or fluff.

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, read-only tool with no output schema, the description is nearly sufficient: it names the main content areas and provides an equivalence to the downloadable file. It could additionally note when to choose this full document over a sibling tool, but that is usage guidance rather than a core completeness 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?

The input schema has zero parameters, so there is nothing for the description to document beyond what the schema already shows. The description appropriately focuses on what the returned context document contains rather than 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?

The description clearly identifies the resource as 'the complete weekly context document' and enumerates its contents (scores, tracked questions, mentions, next steps). It does not use an explicit verb like 'retrieves' or 'returns', and it does not explicitly distinguish itself from sibling tools, though 'complete' implies the full-document role.

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 context is only implied: the phrase 'complete weekly context document' and 'Same content as the downloadable context.md' suggest this tool is for getting everything at once, while sibling tools like get_answers and get_mentions cover narrower slices. There is no explicit when-to-use or when-not-to-use statement.

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 annotation, the description reveals that the result comes from stored evidence, includes quoted robots.txt rules, and discloses the tool's inability to assess some vendors—transparency that prevents misinference.

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 a single, well-structured sentence plus a short second sentence. It packs in necessary detail without fluff, though it is slightly long due to the crawler enumeration.

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 tool with no output schema and minimal annotations, the description fully explains what the agent will receive (per-crawler allow/block status, purpose split, quoted rules, and unassessable vendors). 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?

There are no parameters, and the schema covers 100% trivially. The baseline for zero parameters is 4, and the description adds nothing about parameters because none exist.

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 specific verb ('whether ... allows or blocks') with a precise resource (robots.txt) and enumerates the exact set of AI crawlers and purposes. This distinguishes it from the sibling get_* tools, none of which cover crawler access.

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 clearly indicates the data source (latest deep scan) and warns about unassessable vendors so the agent does not misinterpret silence. It doesn't explicitly state when not to use it or name alternatives, but the unique scope makes this unnecessary.

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 declare readOnlyHint=true, and the description adds that the data comes from the latest deep scan, implying a dependency on a scan having been performed. This is extra context beyond the read-only annotation, but it does not disclose what happens if no scan exists or if the scan is stale.

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?

A single, front-loaded sentence that conveys the purpose, content, and ordering without extraneous detail. Every clause earns its place, and the structure is efficient for an agent to parse quickly.

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 simple read-only tool with no parameters and no output schema, the description provides sufficient context: what it returns (prioritized fix list) and its ordering. It doesn't mention potential edge cases like absence of a scan, but that is acceptable given the tool's simplicity and the presence of sibling request_rescan.

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 has zero parameters and an empty schema, so there is nothing to describe. Baseline for 0 params is 4; the description adds no parameter-related information, which is appropriate given the schema coverage is 100% and no params exist.

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 clearly states the tool returns a prioritized fix list from the latest deep scan, specifying the content (specific changes to improve AI answers) and ordering (highest impact first). This distinguishes it from sibling tools like get_answers or get_benchmark, which focus on other data.

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 implies usage after a deep scan ('latest deep scan') but does not explicitly state when to use this tool over alternatives or when not to use it. No exclusions or alternative tool names are provided, leaving the agent to infer context 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?

Annotations already declare readOnlyHint=true, and the description adds useful context about the 'daily sweep' and that only new pages are included. However, it does not disclose other behavioral details such as output format, pagination, or the exact definition of a 'mention.'

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 short sentences with no wasted words. The main function is front-loaded, and the second sentence earns its place by explaining why mentions matter, aiding the agent's decision to use 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?

For a simple read-only list tool with one optional parameter, no output schema, and a safe annotation, the description captures the core purpose and data source. It leaves minor gaps around the structure of returned entries and exact inclusion criteria, but overall it is sufficiently complete for correct invocation.

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%: the only parameter 'days' is fully described with a range and default. The description adds no additional parameter semantics, so the baseline of 3 applies because the schema does the heavy lifting.

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 clearly states the tool returns 'new pages on the web that mention the business, from the daily sweep,' identifying the operation and resource. It implicitly differentiates from siblings like get_citation_sources or get_share_of_voice, though it does not name them explicitly.

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 implies usage for tracking recent web mentions and highlights their importance as a strong signal for AI answers, but it does not specify when to prefer this tool over alternatives or provide exclusion criteria. The usage context is present but not explicit.

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 goes beyond the readOnlyHint annotation by explaining that the personas are inferred, that the count ranges from 2-4, that the style is how AI models would describe buyer types, and that exact tracked questions are included. This added context clarifies what kind of data to expect and how it was derived, with no contradiction to 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?

The description is a single concise sentence that front-loads the core output ('2-4 buyer personas') and packs in source, style, and included data without filler. The appended 'Read-only' is redundant with annotations but does not harm conciseness.

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 parameterless, read-only tool with no output schema, the description tells the agent everything needed to know before calling: the range of personas, their origin, the descriptive style, and that each persona carries its tracked questions. No critical behavioral or shape information 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 has zero parameters, so the empty schema fully covers parameter expectations. Per the baseline for 0-parameter tools, the description does not need to explain parameters; it adds no parameter details but none are required.

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 clearly states that the tool returns 2-4 buyer personas inferred from measured questions, describes how they are framed (as AI models would describe each buyer type), and notes each persona includes the exact tracked questions they ask. This is a specific, unambiguous definition of the resource and its content, and it is distinct among the sibling get_* 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?

The description provides no guidance about when to use this tool versus the many sibling tools. It does not state a use case, prerequisites, or alternatives, so an agent must infer appropriateness solely from the name and content description.

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 the tool as read-only, and the description adds meaningful behavioral context: it returns a brief, never generated copy, and explicitly assigns drafting to the caller's AI in the business's voice. This clarifies the tool's boundary beyond the simple readOnlyHint.

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 earns its place: the first establishes purpose and contents, the second clarifies the output boundary, and the final phrase grounds the division of labor. There is 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?

For a parameterless, read-only tool, the description provides enough detail about what is returned and what the brief contains. Without an output schema, it still communicates the key return semantics and leaves no critical gap for an agent deciding whether to call this tool.

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 has zero parameters, so the schema provides complete coverage. The description does not need to elaborate on parameter semantics, and the baseline 4 applies here.

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 a specific deliverable—a drafting brief for social posts—assembled from measured data, and explicitly distinguishes itself from copy generation. This separates it clearly from sibling get_* research tools, which return raw data views rather than a ready-to-use drafting brief.

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 intended use case is implied: an AI needs this brief when drafting social posts to improve AI visibility. However, the description does not explicitly state when not to use it or name sibling tools as alternatives, relying on the reader to infer context.

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?

Read-only annotation already signals safety, and the description adds meaningful behavioral context: it returns a time-series-like history across measured days for all tracked buyer questions, counting engine mentions. It does not describe output formatting or pagination, but the absence of parameters and read-only nature keep this a minor 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?

Two sentences and the core definition is front-loaded. The second sentence is slightly rhetorical but reinforces the granularity and strategic relevance, so it mostly earns its place without adding hard operational detail.

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 zero parameters, a read-only annotation, and no nested schema, the description provides everything an agent needs to invoke this tool correctly and interpret what it represents. 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 has zero parameters and the schema coverage is 100%, so there is no parameter ambiguity. Baseline 4 applies because there is no parameter-usage burden for the agent to overcome.

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 a specific resource (tracked buyer questions) and a precise metric (how many of the 7 engines named the business on each measured day). It also distinguishes itself from aggregate siblings like get_visibility and get_share_of_voice by emphasizing question-by-question breakdown.

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 implies this is for per-question visibility history rather than aggregate metrics, and the closing sentence reinforces the granular context. However, it does not explicitly name sibling tools to avoid or state when not to use it, so it stops short of full alternative 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 mark the tool as readOnlyHint=true, and the description adds value beyond that by explicitly stating 'No fresh provider requests' – confirming it does not trigger external API calls. It also clarifies the data semantics ('Unknown and failed reads are distinct from not named'), which is a meaningful behavioral disclosure about how missing data is represented. This goes beyond the annotation and provides useful context.

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 only one long sentence, which is brief, but it is awkwardly phrased and packs multiple concepts into a comma-separated list. It is not front-loaded with a clear verb or resource; the most important action (returning saved observations) is never explicitly stated. It could be reworded into clearer, more structured sentences.

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 tool has no parameters and no output schema, the description covers the cached nature and data distinctions, but it still lacks an explicit statement that this tool retrieves prior regional observations. It also does not mention any limits (e.g., how much data, time range, or format), leaving some gaps for an agent deciding to call it. It is minimally complete but not thorough.

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 has zero parameters, and the schema is an empty object with 100% coverage by definition. Per the calibration baseline, a 0-parameter tool defaults to 4. The description does not need to explain parameters, and it doesn't attempt to, which 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?

The description lists what is 'saved' (areas, buyer questions, engine naming observations, retained samples, read coverage) and states 'No fresh provider requests,' implying a cached retrieval. However, it never explicitly uses a retrieval verb like 'returns' or 'gets,' leaving the action to inference from the tool name. It also does not distinguish itself from sibling tools like get_visibility, so purpose is clear but not sharply defined.

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 provides no direct guidance on when to use this tool versus alternatives. The statement 'No fresh provider requests' hints that this is a cached snapshot, which could imply using request_rescan for fresh data, but this is not stated. There is no explicit when-to-use or when-not-to-use guidance, leaving the agent to guess.

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 readOnlyHint annotation already covers the tool's safety profile, so the description does not need to restate that. It does add useful context about the data scope (latest scan) and the condition under which these competitor names appear, but it does not describe return format or other behavioral details.

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 a single sentence with no filler and gets to the core concept quickly. It is slightly awkward and the title 'Competitors AI names instead' adds no clarity, but the description itself remains compact.

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 read-only tool, the description sufficiently explains what data will be returned: competitor names from answer engines under a specific condition. It does not describe output structure, but no output schema is provided and the statement is adequate for selection and invocation.

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 has zero parameters and the schema is fully described by its empty object, so there are no parameter semantics for the description to clarify. The baseline of 4 applies because no parameter documentation burden exists.

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 clearly identifies the resource (competitor names given by answer engines) and the specific condition (when they did not name this business). It distinguishes itself from siblings like get_mentions, which would cover mentions of the business itself, though it lacks an explicit verb and the phrasing is somewhat awkward.

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 guidance is provided about when to use this tool instead of related siblings such as get_share_of_voice, get_mentions, or get_answers. The intended use is implied by the name and description, but there is no explicit context or alternative 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 provide readOnlyHint=true, and the description adds useful context beyond that: it reads stored evidence only, performs no provider request, and supports pagination with offset and limit. This adds behavioral details not captured by the annotation alone, though it could still note edge cases like empty results.

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 two concise sentences, front-loading the main purpose and then noting the read-only nature and pagination. Every sentence earns its place with 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?

The tool has no output schema, so the description should explain return values; it lists what the evidence contains (read dates, incomplete states, excerpts, limitations) but not the structure or format. It also leaves the 'chars' parameter unexplained. Given the tool's moderate complexity (3 params, no output schema), the description is only partially 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 mentions 'offset and limit' in the context of pagination, but does not explain the 'chars' parameter at all, nor does it give semantics for offset/limit beyond the schema's own constraints. This is insufficient for a tool with zero schema 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 states a specific verb ('Reads'), a clear resource ('stored evidence' comparing initial HTML and rendered JSON-LD), and details what the evidence contains (read dates, incomplete states, excerpts, limitations). It also distinguishes itself from siblings by specifying it reads stored evidence only, making its purpose unambiguous.

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 clarifies that it reads stored evidence and makes no provider request, which is a usage constraint. However, it does not explicitly guide when to use this tool versus alternatives, nor does it mention any conditions or exclusions. The context is clear but lacks explicit routing to or away from sibling 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?

With readOnlyHint annotation already present, the description adds context about the two measurements being kept apart and the source of the web-scale index (ChatGPT answers). It also clarifies that the comparison set is not a rival list, which is a useful behavioral nuance beyond the annotation.

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 three sentences, front-loaded with the key idea ('Two measurements, kept apart.'), then explains each measurement and provides a pointer to get_rivals. No wasted words, and the structure is logical and efficient.

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 no-parameter, read-only tool without an output schema, the description fully explains what the tool measures and when to use it, including the distinction from get_rivals. Nothing essential is missing for an agent 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?

The tool has zero parameters, so schema coverage is 100% by default. The description doesn't need to explain parameters, and the baseline for 0-parameter tools is 4. It adds contextual information about the two measurements without needing parameter details.

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 explicitly states the tool measures two distinct metrics: share of mentions in tracked answers against named rivals, and web mention share against compared brands. It uses a specific verb and resource, and clearly distinguishes itself from get_rivals by naming that sibling and explaining the difference.

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 provides explicit guidance: 'Use get_rivals for who the engines name instead of you.' This tells the agent when to use this tool versus a specific alternative, and clarifies that the second metric's comparison set is not a rival list.

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 complements this by explaining the data is aggregate measurement-corpus data and that 'a thin result means the engines rarely cite it.' It also clarifies what the 'profile' contains, including the caveat that the listing page is included only 'where our crawler found one.' This adds interpretive context beyond the read-only annotation without contradicting it.

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 front-loads the core idea—'A profile of any domain the answer engines cite'—then uses a colon and parentheticals to compactly enumerate result contents. The second sentence adds measurement-context and result interpretation, earning its place. Slightly long and the title is diluted ('What a cited source is and how to get on it'), but the body is dense rather than padded.

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 one required parameter and readOnlyHint=true, the description covers what the agent gets back (frequency, count coverage, homepage title/description, co-read pack, listing page) and how to interpret sparse results. No output schema exists, so the inline enumeration is the main return-value documentation and it is adequate. Missing explicit when-to-use guidance against siblings, but the tool is simple enough that the gap is not severe.

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 schema already defines `source` as 'The source domain, e.g. canstar.com.au or yelp.com.' The description's phrase 'any domain the answer engines cite' reinforces that the parameter is a citing domain, but it adds no format, normalization, or edge-case semantics beyond the schema. Baseline 3 applies because the schema carries the parameter 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?

The description opens with 'A profile of any domain the answer engines cite' and then enumerates specific content categories (frequency, business/category coverage, homepage title/description, o-read pack, listing page), making the tool's purpose concrete. It doesn't explicitly contrast with sibling tools like get_citation_sources, but the resource (a single domain's profile) and output categories are unambiguous. The verbose title adds little, but the description body compensates.

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 implies this tool is for inspecting one cited domain's profile, but it never states when to choose it over siblings like get_citation_sources or get_visibility. There is no when-not-to-use guidance or alternative routing. An agent must infer the use case from the data-listing alone.

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?

readOnlyHint already establishes this is a safe read, so the bar is lower, and the description adds genuinely useful interpretive behavior: half-credit for list/citation/source-title-only mentions, older results retaining their recorded basis, overlapping flag types, and the important caveat that 'Unavailable is not a negative result.' It omits any note on freshness, auth scope, or how often scores update, but the added semantics are substantial.

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 first sentence front-loads the payload well, but the remainder runs to seven more sentences of glossary-style definitions (Named/Cited/Listed/Found via search) that read as documentation for return values rather than selection guidance. Much of it is defensible domain semantics, yet the block is dense and repetitive for a description, so it is adequately sized but not disciplined.

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 explaining what comes back, and it does so thoroughly: score composition, subscores, prior-week comparison, and the meaning of each positive-result type. The main omission is usage/routing context rather than return-value context, so the description is close to complete for a zero-parameter read tool.

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 per the rubric the baseline is 4. The description does not need to compensate for any schema gap, and it correctly avoids inventing parameter behavior for a no-arg call.

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 a specific verb+resource (latest AI Visibility and AI Readiness scores for areyoufoundbyai.com) and enumerates exactly what comes back: prior-week scores, the off-site Footprint score, and five named subscores. That is far more concrete than a restated title. It does not, however, distinguish this from close siblings such as get_regional_visibility or get_share_of_voice, 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?

There is no when-to-use statement, no exclusion, and no mention of any alternative tool. With 19 sibling get_* tools covering visibility-adjacent data (regional visibility, share of voice, citations, mentions), an agent gets no routing help at all. The body instead defines scoring vocabulary, which is interpretation guidance rather than selection 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?

The description adds useful behavioral context: it consumes a capped on-demand re-measure, runs the measurement, changes nothing else, and takes a few minutes. However, it undermines this by first saying 'queues nothing,' which conflicts with the rest of the description. This internal contradiction makes the tool's actual side effects unclear.

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 relatively short and front-loads key behavioral details, but the first sentence is confusing and seems to contradict the rest. That sentence does not earn its place and adds ambiguity rather than clarity, preventing a higher score.

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 zero parameters and no output schema, the description covers important context: trigger timing, quota consumption, async results, and the follow-up tool. However, it fails to resolve the demo-vs-live ambiguity and does not describe what the worked example response looks like, leaving gaps in 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?

There are zero parameters and schema coverage is 100%, so the description has no obligation to explain parameter semantics. A baseline of 4 is appropriate since no parameter information is missing and the description correctly avoids inventing parameter details.

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 clearly states the intended action: 'Queue a fresh deep measurement right now instead of waiting for the weekly scan.' However, the opening sentence 'DEMO: returns a worked example of the response and queues nothing' directly contradicts that purpose, leaving the agent uncertain whether this tool actually queues a rescan or merely demonstrates the response.

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 a clear usage context: use when you need a fresh deep measurement instead of waiting for the weekly scan, and follow up by reading get_visibility again. But it does not explicitly say when not to use it, discuss plan/cap limitations, or name alternatives beyond the implied get_visibility follow-up.

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