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 already mark the operation read-only (readOnlyHint=true), and the description adds useful behavior: results are the exact engine answers, answers are trimmed to chars, and filtering by substring/engine is available. It also discloses what data is returned without contradicting 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 compact, front-loads the core purpose, and then uses a dense field list that conveys return shape in a single breath. No sentence is wasted and the unit context is included 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?

Because there is no output schema, the description carries the responsibility of explaining the return shape, and it does: engine, model, date, naming/samples, live-search vs memory, competitors, fan-out searches, and Google demand. Combined with complete parameter schema coverage, an agent has enough to select and call 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 description coverage is 100%, so all four parameters are already documented in the schema. The description's mention of filtering by question substring or engine and trimming to chars mostly restates existing parameter descriptions and does not add new semantic 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?

Description clearly identifies a specific resource: exact verbatim answers from each engine for each tracked buyer question in the latest measurement. It distinguishes itself from siblings by framing the result as the 'receipt behind every score' and enumerating answer-level fields, which separates it from get_citation_sources, get_context, and get_mentions.

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 phrase 'receipt behind every score' implies use when raw answer-level evidence is needed, and the filtering note says how to narrow results. However, there is no explicit statement of when to use get_answers versus alternatives or exclusions, so the guidance is implied rather than stated.

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?

The readOnlyHint annotation already signals a safe read operation, and the description adds meaningful context beyond that: the data comes from the latest deep scan, is prioritised, and is ordered by highest leverage first. This gives agents useful expectations about the result's nature and ordering.

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, tightly written sentence that front-loads the core identity ('prioritised fix list') and then adds the key explanatory detail. Every word contributes value with no 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 simple, zero-parameter, read-only tool with no output schema, this description is complete: it states the source, the content, and the ordering. An agent can correctly decide when to call it and what to expect in return.

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 there is no parameter semantics gap for the description to fill. The empty input schema already communicates that no arguments 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 identifies the resource as the prioritised fix list from the latest deep scan and explains what it contains: specific changes to improve AI answers. It is distinguishable from the sibling get_* analytics tools, though it lacks an explicit retrieval verb like 'returns' or 'gets'.

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 is used after a deep scan, but it does not explicitly state when to use this tool versus any alternatives. There is no when-not-to-use guidance or comparison to 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?

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?

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 already provide readOnlyHint=true, and the description adds meaningful behavioral context: it is measured from real answers, not estimated, and includes movement over the tracked window. This goes beyond the schema and helps the agent set expectations about data source and output shape, even though return format details are not spelled out.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences with no wasted words. The core metric is front-loaded, and the data-quality note ('Measured from real answers, not estimated') earns its place as a distinguishing 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?

With no output schema, the description explains what the tool computes and that there is a trend component. The tracked window and exact competitor selection are left vague, but for a zero-parameter, read-only metric this is sufficient for an agent to invoke 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 input schema is empty, which is the baseline-4 case. There are no parameters to describe, and the description correctly does not invent parameter guidance. The schema already communicates that no arguments 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 states a specific metric (share of AI-answer mentions), the scope (you and competitors the engines name in your space), and a temporal component (movement over the tracked window). This clearly distinguishes it from sibling tools like get_mentions (raw mentions) or get_rivals (competitor list).

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 explicit when-to-use or alternative-routing guidance is given. The context implies it is for competitive share comparison, but it does not tell the agent when to prefer this over get_benchmark, get_mentions, or get_rivals. Usage is implied rather than explained.

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?

The readOnlyHint annotation already covers safety, and the description adds meaningful behavioral context: how named answers vs. citation-only mentions count toward visibility, that Footprint is separate, and which subscores are included. 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?

The description is front-loaded with the main result and packs relevant detail into a dense but readable sentence. The list of subscores could be formatted more clearly, but no sentence is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, read-only tool with no output schema, the description fully enumerates what is returned: current scores, previous week's scores, Footprint, and the specific subscores. It also explains the visibility scoring nuance about full mentions vs. citation-only mentions, which is essential for interpreting the result.

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 there is nothing to document or compensate for. The schema coverage is complete by default, and the description adds no parameter info because none 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 the tool returns AI Visibility and AI Readiness scores for areyoufoundbyai.com, including the previous week's scores, off-site Footprint, and detailed subscores. This distinguishes it from sibling get_* tools by identifying its specific metric family and contents.

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 is implied: retrieve visibility and readiness scores for the site. However, the description does not explicitly explain when to choose this tool over siblings like get_benchmark, get_share_of_voice, or get_agent_view, nor does it state any 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?

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.