Adaga solutions — Data APIs Toolbox

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

With no annotations, the description carries the full burden and delivers: it discloses pricing (FREE, no per-run charge), auth requirements (Apify token via Bearer), rate limits (60 calls/min/IP), and return shape (0-7 score, fixes, llms.txt draft, full report in one response, no pagination). This is above the bar expected for an unannotated read tool.

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

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

The purpose and the key alternative are front-loaded in the first two sentences, and the trailing operational facts (pricing, auth, rate limit, no pagination) are compressed into short clauses. It is dense but nearly every clause carries actionable information; a minor trim of the pricing/auth restatement would tighten it further.

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?

No output schema or annotations exist, yet the description compensates fully: it explains the return value (0-7 score plus fixes and an llms.txt draft), scope boundaries, the sibling alternative, auth, rate limits, and response shape. An agent has everything needed to call and interpret 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?

Schema description coverage is 100% and both parameters are well documented in the schema itself. The description reinforces behavior (fetching homepage, /llms.txt, /robots.txt; generating an llms.txt draft) but adds no syntax or format detail beyond what the schema already provides, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Check) and resource (a website's AI visibility) and enumerates the 7-criteria AEO audit contents (JSON-LD, llms.txt, AI bots in robots.txt). It explicitly distinguishes itself from the sibling local_presence_audit, so an agent can route correctly without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit when-to-use trigger ('when you need to know if a site can be cited by ChatGPT, Perplexity or Claude') and a hard exclusion with an alternative: 'This audits ONLY the website... use local_presence_audit instead when you need the full local-business picture'. The when/when-not/alternative triad is fully covered.

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 no annotations, the description carries the full burden and does meaningful work: it discloses the pricing model ($0.50/run + $0.005/API), the return shape (change events classified by severity), and the underlying mechanisms (oasdiff diffing vs rule-based classification). It does not explicitly state read-only/safety characteristics, but the monitoring framing makes mutation risk clear.

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

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

Four compact sentences that front-load the purpose before moving to usage, return, and cost. The 'radar for the APIs you depend on' phrase is mild flavor but earns its place as shorthand; nothing is wasted overall.

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?

No output schema exists, so the description usefully summarizes the return as severity-classified change events, and it discloses pricing, which annotations do not cover. For a 4-parameter monitoring tool with full schema coverage, this is nearly 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 100%, so all four parameters (apis, dryRun, maxApis, webhookUrl) are already fully documented in the schema. The description adds no syntax or format detail beyond what the schema provides, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: monitors public APIs and detects breaking changes by diffing OpenAPI specs and classifying changelogs. Clearly distinguishable from all siblings, which are unrelated scrapers and checkers.

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?

Explicitly names the triggering condition: 'Use when you need early warning before an API change breaks your integration.' Sibling tools are unrelated, so no alternative routing is needed, but it offers no when-not to run guidance (e.g. no-change cost considerations).

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 no annotations, the description carries the full burden and does well: it discloses the data origin (official open data), the return shape (station objects with prices per fuel type), and the cost model ($0.20 per run + $0.01 per 1k stations). It omits any auth or rate-limit detail, leaving a small 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?

Four tight sentences: purpose, usage, return shape, pricing – each front-loaded and earning its place. 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?

No output schema exists, but the description explains the return unit (station objects with per-fuel prices) and the cost per call. Combined with 100% schema coverage for params, an agent has nearly everything, though auth and pagination behavior remain unstated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so both parameters (carburant, departement) are already fully documented in the schema with enums and examples. The description adds no parameter-level meaning beyond what the schema provides, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Get live fuel prices'), names the geographic scope (France), the coverage (~10,000 stations) and the data source (Hermès Revenu API). An agent knows exactly what this returns without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit use cases: comparing fuel prices, estimating route costs, building price datasets for France. No sibling tool overlaps (the others are scrapers/audits), so no alternative routing is needed, but there is no when-not-to-use 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?

With no annotations, the description carries the full burden and does: return shape (array of business objects, everything in one response, no pagination, truncated beyond 100 items), auth requirement (Apify API token via Authorization: Bearer), rate limit (60 calls/min/IP on this facade), and pricing ($1.00 per run + $0.03 per business scraped). This is unusually rich disclosure for a scraper.

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

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

Front-loaded with purpose and scope, then usage routing, then operational facts (auth, rate limit, pricing) — a sensible order with no filler. It is dense and runs long, but nearly every clause carries information an agent needs; the only fat is the parenthetical alternative list, which is still justified.

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?

No output schema exists, and the description compensates by describing the return value (array of business objects, single response, truncation at 100 items). Combined with auth, rate-limit and pricing disclosure, an agent has everything needed to call it correctly and predict the cost.

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 7 parameters are already documented, which sets the baseline at 3. The description adds the extracted-field list and the advice to 'refine the query for narrower results', but says nothing about the queries vs. searchQuery distinction, maxReviews, language, or proxyConfiguration beyond what the schema states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Extract) and resource (local business data from Google Maps) and enumerates the exact fields returned (names, addresses, phones, ratings, review counts, websites, GPS coordinates) plus scope (up to 100 businesses per call). It explicitly names the two siblings it is not (local_presence_audit, review_radar), so an agent can route without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit when-to-use (lead lists, competitor maps, market research across a city or query — MANY businesses as raw data, no scoring or audit) and explicit when-not with named alternatives: local_presence_audit for a deep scored audit of ONE business, review_radar for detecting only NEW reviews on already-tracked businesses. The condition selecting each alternative is spelled out, leaving nothing to inference.

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

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

With no annotations, the description carries the full burden. It discloses that this is a read-style monitoring operation returning observations per hotel and date, which establishes the safety profile implicitly. It omits which booking sources, data freshness, rate limits, and auth requirements — meaningful gaps for a no-annotation tool.

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

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

Four short sentences with the primary purpose front-loaded and no filler. The closing fragment "Micro-pricing per event." is cryptic and undefined, weakening an otherwise tight structure.

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

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

No output schema exists, so the description must frame the return value; it does say "rate observations per hotel and date," which is the minimum. But with no parameters, no annotations, and no explanation of sources, coverage or cadence, and the unexplained "micro-pricing per event" clause, an agent still lacks enough to invoke with confidence.

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 declares zero parameters, so there is nothing for the description to disambiguate; the baseline for a 0-param tool is 4. The description's mention of hotels, dates, and sources is conceptual rather than parameter-level detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (monitor) and resource (hotel rates) plus scope (across booking sources) and audience (revenue management). No sibling tool overlaps with hotel pricing, so differentiation is implicitly strong, though the description never anchors itself against the scraper/review siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

"Use when tracking competitor hotel pricing or building rate intelligence" gives a clear triggering context. However, it names no alternatives or exclusions, so an agent gets no routing guidance for the adjacent scrapers or review_radar.

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 no annotations the description carries the full burden, and it does add real behavioral value: it discloses the return shape ('profile objects with recent posts') and a cost model ('$0.01 per profile scraped'), which an agent needs in order to budget calls. It omits rate limits, runtime/latency expectations, and failure modes, so it is not complete for a scraper with zero structured safety hints.

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

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

Three sentences, each earning its place: what it scrapes, when to use it, and what it returns plus cost. The purpose and scope are front-loaded and there is no filler or repetition.

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

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

There is no output schema and no annotations, so the description must carry more weight, and it does cover purpose, use cases, returned shape and pricing. It leaves gaps around rate limiting, pagination or result limits, and error behavior for a network-scraping tool, but nothing essential to invoking the single required parameter is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and there is only one parameter, so the schema already explains the profiles list and its format. The description adds no format detail and actually introduces a 'hashtag posts' capability the parameter does not expose, which slightly muddies rather than clarifies parameter meaning. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource ('Scrape Instagram profiles and hashtag posts') plus the concrete fields returned ('followers, posts, engagement metrics'), and the Instagram scope implicitly separates it from tiktok_scraper, youtube_scraper and linkedin_profile_scraper. It falls short of 5 because the 'hashtag posts' claim is not backed by the input schema, which only accepts a profiles array, leaving the agent unsure whether hashtags are actually supported.

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 names clear use cases ('vetting influencers, monitoring brands or tracking competitors on Instagram'), which is well above the implied-usage bar. It stops short of 5 because it never states when not to use it or explicitly names a sibling alternative (e.g., for non-profile content on another platform).

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 no annotations provided, the description must carry the behavioral burden, and it does disclose key operational facts: it targets public profiles only and states a concrete pricing model ($0.02 per profile scraped). It omits other important behavior like rate limits, failure/partial-result handling, or auth requirements.

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

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

Three short sentences, front-loaded with what the tool does, then usage, then cost. Efficient and scannable with negligible waste, though the pricing sentence is appended rather than integrated.

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?

Covers purpose, usage, and the notable pricing behavior for a simple 3-param extraction tool with no output schema. It lacks return-shape detail and failure/limit behavior, but 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?

Schema description coverage is 100% and each parameter (profileUrls, includeSkills, includeExperience) is fully documented in the schema, so the baseline is 3. The description adds no syntax or format detail beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Extract) plus resource (public LinkedIn profile data) and enumerates the fields returned (name, headline, experience, company). It is immediately distinguishable from its sibling scrapers (instagram_scraper, tiktok_scraper, youtube_scraper) by naming the target platform 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?

Gives clear use contexts ('enriching B2B leads', 'recruiting research'), which tells the agent when the tool is relevant. It does not, however, name alternatives or state exclusions (e.g., non-public profiles, rate limits), so it stops short of full when/when-not 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?

With no annotations, the description carries the full behavioral burden and does so: it discloses the required Apify auth scheme (Authorization: Bearer), rate limits (60 calls/min/IP), the 100-item cap with no pagination, and the pricing model ($1.00 per run + $0.50 per business audited). It also explains that the website is scanned 'without a browser', which tells the agent the scan is passive. This is unusually rich disclosure for a no-annotation tool.

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

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

It is front-loaded: purpose first, then the trigger, then sibling routing, then operational constraints. The first sentence is very dense with a long deliverables list, and the auth/rate/pricing tail is compact but adds bulk. Efficient overall, though the opening could be trimmed slightly.

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

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

There is no output schema and no annotations, so the description must cover purpose, routing, outputs and operations — and it does all four, even enumerating the concrete deliverables (llms.txt, LocalBusiness JSON-LD, AI-drafted review responses, action plan). An agent has everything needed to decide and invoke 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 the schema already documents all six parameters (query, dryRun, language, maxReviews, websiteUrl, proxyConfiguration) in detail. The description adds no parameter-level syntax or semantics beyond what the schema provides, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence names a specific verb+resource ('Deep audit of ONE local business's online presence in a single run') and enumerates exactly what the audit covers: Google Business Profile data, reviews, website AI-readiness, a /100 score, and deliverables. It is immediately distinguishable from every sibling because it explicitly contrasts itself with google_maps_scraper, aeo_ai_visibility_checker and review_radar.

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 states the positive trigger ('before selling it marketing services', checking whether ChatGPT/Perplexity/Gemini can see it) and then names three alternatives with the exact condition that selects each: raw LIST of many businesses → google_maps_scraper; website-only technical check → aeo_ai_visibility_checker; ongoing monitoring of NEW reviews → review_radar. Nothing is left to inference.

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

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

With no annotations, the description carries the full burden and does disclose meaningful traits: the return shape ('per-page cleaned text with metadata') and cost ('$0.003 per page crawled'). It omits operational details such as authentication requirements, error/partial-failure behavior, or latency expectations, which is the remaining 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?

Three tight sentences with zero filler, front-loading what the tool does, then when to use it, then return shape and price. Each sentence earns its place and the important routing information comes first.

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?

No output schema exists, so the description rightly covers the return value, and it also notes cost. For a five-parameter tool with a dual query/urls mode, it is nearly complete, though it could say more about what happens when both query and urls are supplied or omitted.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter is already documented in the schema, including the query-vs-urls distinction and the markdown/text enum. The description adds no parameter-level meaning beyond that, making the baseline 3 correct.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Fetch web pages and convert them into clean LLM-ready text') plus the delivery target ('RAG pipelines'). This clearly distinguishes it from the sibling scrapers, which are all platform-specific (instagram_scraper, youtube_scraper, etc.), while this one is the generic page-content fetcher.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a clear usage condition: 'Use when an agent needs readable page content from the web without HTML noise — a lightweight browser alternative for retrieval.' It frames the retrieval use case well but names no excluded cases and points to no alternative tool for adjacent needs (e.g. structured scraping), so an agent still infers the boundary against 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?

No annotations are provided, so the description carries the full burden and does so: it discloses the outbound email side effect, the exact gate (sendApproval:true plus smtpUser/smtpPassword), the default-safe behavior, three separate rate limits (5/run, 20s interval, 20/day), auth requirement, facade limit, and pricing. This is materially more than the schema exposes.

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

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

One dense paragraph, front-loaded with purpose then alternatives then side effects then auth/pricing — a sensible priority order with no filler sentences. It is long, and the sendApproval gate is restated in both the description and the schema, which is mild redundancy rather than waste.

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 15-parameter tool with no annotations and no output schema, the description covers returns ('pitch text and a shareable report URL'), the destructive/outbound path, credentials, limits, and cost. The mode enum's two behaviors (generate vs check-engagement) are left entirely to the schema, which is the only real gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3, but the description adds semantics the schema states only locally — that maxEmailsPerRun is bounded by an inter-send delay and a global daily cap, and that the SMTP path only activates under an explicit approval gate. It adds value beyond the field-level text without fully re-documenting all 15 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?

Starts with a specific verb+resource pair ('Generate pre-sales reports from a business's worst Google Maps reviews') and immediately distinguishes the output (HTML report plus ready-to-send pitch). It also names three sibling tools it is NOT (review_radar, local_presence_audit, google_maps_scraper), so an agent can route without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit when-to-use ('agency outbound... show prospects their visible bad reviews') and three explicit when-not cases with the alternative tool named for each. Nothing is left to inference about selection against 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?

With no annotations, the description carries the full burden and delivers: it discloses stateful tracking across runs, conditional Slack side effects (only if the caller supplies a webhook/connector), required Apify auth, a 60 calls/min rate limit, and a pricing model. This is behavior an agent cannot infer from the schema.

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

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

Front-loaded with purpose, then routing, then returns/side effects/auth/limits/pricing in scannable clauses. Dense but every sentence conveys a distinct operational fact; only the slogan 'your reputation radar' is filler.

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 an 8-param stateful actor with no output schema and no annotations, the description covers everything needed to call it correctly: statefulness, dry-run availability, alerting precondition, auth, rate limit, pricing, and the return shape (review objects flagged isNew with urgency scores).

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 schema already documents query, maxPlaces, alertThreshold, dryRun, placeUrls, etc., and the baseline is 3. The description adds only marginal param context — the Slack-webhook-optionality and the isNew return flag — without restating the individual field meanings.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (monitor) and resource (Google Maps reviews) with a precise scope: only NEW reviews, stateful per business, with Bayesian urgency classification. It explicitly names the siblings it is not (google_maps_scraper, review_pitch_generator, local_presence_audit) and the condition that distinguishes each.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit when-to-use ('tracking review activity OVER TIME for businesses you already know') plus three named alternatives with the exact scenario each covers (one-shot bulk extraction, sales pitch from bad reviews, one-time presence audit). Nothing is left to inference.

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

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

No annotations exist, so the description carries the full burden. It does add genuine value beyond structured fields: explicit per-call pricing ($0.01/profile + $0.002/video) and the shape of the result objects. It omits auth/API-key requirements, rate limits, and how the required 'profiles' param interacts with the optional 'hashtags' mode.

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

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

Three tight sentences, front-loaded with capability, then usage, then return shape, then cost. Nothing is padding — even the return-type sentence earns its place because there is no 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 and no annotations, the description usefully covers returns, cost, and call context. The main omission is the profiles-vs-hashtags usage relationship, which an agent needs to invoke the tool correctly in hashtag-only scenarios.

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 three parameters are already documented in the schema; baseline 3 applies. The description adds no param-level meaning, notably failing to clarify the apparent tension between the required 'profiles' field and the hashtag-only trending-videos use case.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (scrape) and resource (TikTok profiles and videos) and enumerates the data returned (follower counts, likes, video metadata and stats). The platform name alone distinguishes it from the instagram_scraper, youtube_scraper, and linkedin_profile_scraper siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

"Use when researching influencers, tracking social engagement or analyzing TikTok content" gives clear triggering contexts. However it never states when NOT to use it and never names the sibling scrapers (instagram_scraper, youtube_scraper) as the alternatives for non-TikTok platforms, leaving that inference to the agent.

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?

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses pricing ($0.002 per video scraped) and the shape of the return (video and channel objects), but says nothing about auth requirements, rate limits, quota limits, or error behavior for a paid third-party scraping service.

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

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

Four short sentences, front-loaded with the action and the returned fields, then usage context and cost. Every sentence carries information; nothing is 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?

For a 4-parameter read-only scraper with no output schema, the definition covers what it fetches, when to use it, its return type, and its cost. Remaining gaps (auth, rate limits, behavior when combining searchQueries with explicit URLs) are modest given the rich schema coverage.

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 (videoUrls, channelUrls, searchQueries, maxResults) are already fully documented, including accepted URL formats and the 1-50 range. The description adds no parameter-level detail beyond it, making the baseline 3 appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (scrape) and resource (YouTube videos and channels) and enumerates the data returned (views, subscribers, metadata, descriptions). The platform-specific framing cleanly distinguishes it from the platform siblings like tiktok_scraper and instagram_scraper.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Use when researching content trends, analyzing creators or building YouTube datasets' gives clear contextual triggers for selection. It does not name exclusions or say when a sibling scraper would be preferred, so it falls short of a 5.

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