MSAI 17 Apify M2M Suite

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

With no annotations, the description carries the full burden. It usefully discloses the x402 M2M cost (0.01 USDC on Base) and the shape of the return (ranked items with prices, ratings, ranks), but says nothing about rate limits, pagination, scraping reliability, or failure behavior.

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 Trigger/Input/Output structure is front-loaded and easy to scan, and each clause carries information. The 'Amazon Bestsellers & Category Scraper:' prefix mildly duplicates the tool name but is not a serious 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 single-parameter scraping tool with no output schema, the description supplies the trigger context, the input, and the output fields. It is sufficient to invoke correctly, though it omits rate-limit and result-volume expectations that an agent might want.

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?

Only one parameter and schema description coverage is 100%, so the schema already documents 'category'. The description restates it with the same 'electronics' example, adding no syntax, format, or valid-value detail beyond the schema. Baseline 3 is appropriate.

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

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

The description states a specific verb and resource (scrapes ranked Amazon bestseller items) and scopes it to Amazon retail categories, distinguishing it from shopee_scraper and similar siblings. It stops short of explicitly differentiating from the overlapping ecommerce_price_tracker, so it is clear but not fully differentiated.

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 'Trigger: Call this tool when an agent monitors product trends, sales rankings, or pricing...' clause gives a concrete use context. It offers no explicit when-not-to-use guidance or named alternatives for the pricing overlap, 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.

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

No annotations are provided, so the description carries the full burden. It usefully discloses the x402 M2M cost (0.02 USDC on Base) and the shape of the output, but says nothing about authentication, rate limits, failure modes, or whether enrichment is read-only against the target domain.

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

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

Front-loaded with the tool's identity and organized into Trigger / Input / Output / Cost segments, so an agent can skim to the relevant part. The parenthetical cost note is compact rather than wasteful.

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

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

For a single-parameter, no-output-schema tool, the description covers trigger, input format, output shape, and pricing, which is close to complete. Remaining gaps are behavioral details (auth, limits) rather than anything about how to invoke it.

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 documents 'domain'. The description repeats the example (stripe.com) and adds the word 'required', adding little beyond what structured data provides; 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?

Names a specific capability (B2B lead enrichment) and enumerates the concrete data it returns: executive contacts, headcount, LinkedIn URLs, and tech stack for a corporate domain. This distinguishes it from generic scrapers like linkedin_scraper or smartscrape_ai, though it does not explicitly name them as alternatives.

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 'Trigger' clause gives an explicit condition for use: when an agent needs verified executive contacts and company data for a corporate domain. It stops short of stating when NOT to use it or naming sibling alternatives, so it is clear context without 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?

With no annotations, the description carries the full behavioral burden. It usefully discloses the output shape and a real operational constraint (x402 M2M cost of 0.01 USDC on Base), which is meaningful beyond structured fields. However, it says nothing about failure behavior on unsupported or bot-blocked URLs, rate limits, or that the call is a non-mutating read.

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 Trigger/Input/Output labeling front-loads the essential information and the whole thing is three compact clauses. The 'Multi-Retailer E-Commerce Price Tracker:' prefix and the parenthetical cost are slightly redundant framing but each clause still carries content.

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 enumerating the return fields is genuinely necessary and it's done. For a single-parameter, no-annotation tool the description covers purpose, input, and output adequately; only edge-case behavior for unsupported sites is unaddressed.

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

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

Schema description coverage is 100% for the single 'url' parameter, so the baseline is 3. The description restates it as '(required product URL)', adding essentially no format or syntax detail beyond what the schema already supplies — and it asserts 'required' where the schema declares no required fields.

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

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

The description names a specific verb and resource ('monitor real-time stock and product pricing') and scopes it to 'any e-commerce URL', which distinguishes it from the single-retailer siblings like amazon_bestsellers, shopee_scraper, and the generic scrapers. An agent can tell what this tool returns (price, currency, availability, discount %) 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?

The 'Trigger' clause gives explicit when-to-use guidance: when an agent needs real-time stock/pricing on an arbitrary e-commerce URL. It stops short of naming alternatives or exclusions (e.g. when to prefer amazon_bestsellers or a site-specific scraper), so it is clear context without 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?

With no annotations, the description carries the full behavioral burden. It does disclose two genuinely useful facts: the return shape (activity metrics plus health score) and a required x402 machine-to-machine payment of 0.01 USDC on Base, which is real invocation-relevant context. It says nothing about data freshness, rate limits, failure modes, or whether owner/repo must exist publicly, so material gaps remain.

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 text is front-loaded with labeled sections (Trigger, Input, Output, Cost) so an agent can skim to the relevant clause, and each sentence carries distinct information. The opening 'GitHub Repository & Developer Intelligence' banner is slightly redundant padding, keeping it from a 5.

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 one-parameter tool with no output schema, the description supplies the input format, the output contents, and the payment requirement an agent must satisfy before calling. That covers the essentials a caller needs, with only secondary details like latency or error behavior 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 a single parameter, so the structured field already explains 'repo'. The description repeats the owner/repo format and the 'facebook/react' example, adding no meaning beyond what the schema and its example provide. 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 names the resource (GitHub repository and developer intelligence) and clearly states what the agent gets back: developer activity metrics and a repository health score. The operation is implied through the trigger sentence rather than an explicit verb like 'get' or 'analyze', which keeps it just short of a 5. No sibling competes in this domain (all siblings target other platforms), so differentiation is implicit rather than explicit.

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 'Trigger' clause gives clear, concrete when-to-use conditions: repository velocity, PR merge speeds, contributor activity, issue volume. There are no competing GitHub tools among the siblings, so exclusion guidance isn't strictly needed. It stops short of a 5 because no when-NOT-to-use or prerequisite condition is 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?

No annotations are provided, so the description carries the full burden. It does disclose genuinely useful behavior: the output shape (structured places with coordinates/phones/ratings) and a machine-payment cost (0.015 USDC on Base). It omits permission/auth mechanics for x402 payment, rate limits, pagination, and error behavior.

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 Trigger/Input/Output framing is front-loaded and scannable, and each section earns its place. It is slightly over-labelled but has no filler prose.

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 annotations and no output schema, the description must carry the load. It covers purpose, inputs, and outputs, but leaves the payment mechanism (how the x402 USDC charge is settled), result caps, and failure modes undescribed for a paid scraping 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 coverage is 100%, which sets a baseline of 3, but the description adds real value beyond the schema by stating that 'limit' defaults to 10 and giving a concrete example query format ('coffee shops in Austin'). The schema only says 'e.g. 10' without establishing a default.

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

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

The description names a specific verb+resource ('Google Maps Local Business & Reviews Scraper') and enumerates the data returned (addresses, ratings, phone numbers, coordinates). This is clearly distinguishable from generic siblings like google_search and review_analyzer, though no sibling is named 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 'Trigger' sentence gives an explicit when-to-use condition: agent searches for local physical businesses, addresses, ratings, phone numbers via Google Maps. It does not, however, state when NOT to use it or name alternative tools (e.g. google_search for general web results).

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 two non-obvious traits: the exact output shape (titles, URLs, organic snippets) and a paid-machine cost of 0.005 USDC on Base. It does not cover rate limits, authentication mechanics, or freshness guarantees, but the cost disclosure is genuinely valuable context an agent cannot get 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?

The description is front-loaded into labeled sections (Trigger / Input / Output / cost), making it very scannable. The opening noun phrase partially duplicates the tool name and the trailing cost parenthetical is loosely attached, but there is little waste 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?

With no output schema, the description correctly compensates by describing the return array (titles, URLs, snippets) and covers inputs, trigger conditions, and payment cost. It is complete enough for correct invocation, though edge behaviors like pagination or error responses are unaddressed.

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

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

Schema description coverage is 100%, so the baseline is 3. The description restates both parameters and adds the default value of maxResults (10), which the schema only implies via an example, but otherwise adds no syntax or formatting 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?

The description states a specific verb+resource ('Real-Time Google Search Scraper' returning organic Google SERP listings), which is unambiguous and inherently distinct from the sibling scrapers (reddit_scraper, linkedin_scraper, etc.). An agent can tell instantly that this is the general web-search tool.

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 'Trigger' clause gives explicit when-to-use conditions: live web search grounding, fresh news, or current organic SERP listings. It lacks any when-not guidance or named alternatives, but the positive conditions are clear and actionable.

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 does disclose useful non-schema behavior: the return shape ('publish status and post URL') and a metered machine-to-machine cost (0.015 USDC on Base via x402). However, it omits whether the post goes live immediately/publicly, what account authorization is required, or any rate limits.

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 and organized into Trigger/Input/Output labels, so the key facts are scannable with no filler sentences. It loses a point because the Input line duplicates the schema and does so inaccurately.

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-annotation, no-output-schema tool the description does cover the output and cost, which is helpful. But the parameter mismatch and the absence of any account/auth or visibility detail leave meaningful gaps for a publish action.

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 description advertises an 'imageUrl' (optional string URL) parameter that does not exist in the input schema, which only defines 'caption'. This is actively misleading rather than additive; an agent may attempt to pass an unsupported argument. Schema coverage is otherwise 100%, so the description has nothing legitimate to add.

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+resource ('publish an image, carousel, or caption ... to an Instagram account') and names the actor (autonomous agent), which cleanly separates it from the read-oriented sibling instagram_scraper. An agent can identify the tool's job 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?

It offers a 'Trigger' condition ('when an autonomous agent needs to publish...'), but this largely restates the purpose rather than contrasting against alternatives or stating exclusions. No guidance on when NOT to use it or what to use instead for related tasks.

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 usefully discloses the paid x402 M2M cost (0.008 USDC on Base), the public-only scope, and roughly what the response contains — real behavioral context. It omits rate limits, private-account failure behavior, pagination, and what errors look like, which matters for a network 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 a templated Trigger/Input/Output breakdown that is easy to scan and free of filler. The structure is slightly formulaic and the Output clause is thin, but every sentence carries information.

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 two-parameter tool with no output schema and no annotations, the description covers trigger, inputs, defaults, output shape, and cost. It does not clarify that 'username' is effectively required despite the schema listing zero required parameters, which is the one remaining 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 coverage is 100%, so the baseline is 3, but the description adds a genuinely new fact not present in the schema: 'limit' defaults to 10. That default changes call behavior and is worth the bump; beyond it there is no syntax or format guidance for the handle.

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+resource ('Instagram Profile & Media Scraper') and enumerates what it retrieves: public account data, bios, follower statistics, and recent reel engagement. An agent can separate it from instagram_poster (write) and from the non-Instagram scrapers in the sibling list without opening any schema.

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

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

The 'Trigger:' clause gives an explicit when-to-use condition tied to needing public Instagram account data. It does not name exclusions or point to an alternative (e.g., when to prefer instagram_poster or a general crawler), so it falls just short of the top band.

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. It usefully discloses the x402 M2M payment model and cost (0.02 USDC on Base) and summarizes the output, but says nothing about auth requirements, rate limits, or scraping constraints. Adequate but incomplete for an unannotated 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 Trigger/Input/Output labeling front-loads the routing cue and keeps the description to a few dense lines with no filler. Slight redundancy in restating the tool name, but efficient 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?

For a single-parameter scraper with no output schema and no annotations, the description covers trigger, input, output, and the cost model, which is enough for an agent to call it correctly. Missing operational details (auth, limits, output format) keep it from a 5.

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

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

There is one parameter at 100% schema description coverage, so the schema already documents it. The description repeats it as a "string company identifier" and the schema supplies the "anthropic" example, so the description adds no format, casing, or URL-slug guidance beyond the structured data. Baseline 3 applies.

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

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

The description states a specific verb+resource: it scrapes LinkedIn public profiles for company hiring trends, open job openings, and headcount growth, and it names its outputs (company overview and active job postings). It is clearly distinguishable from the other scraper siblings, though it does not call out any sibling by name.

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?

"Trigger: Call this tool when an agent needs company hiring trends, open job openings, and headcount growth from LinkedIn public profiles" gives explicit when-to-use context. It offers no exclusions or named alternatives (e.g. vs. github_intelligence or b2b_lead_enricher), so it stops short of a 5.

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

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

With no annotations, the description carries the full burden. It usefully discloses the paid x402 M2M cost (0.01 USDC on Base) and the shape of the return (Markdown with word and token counts), which is genuine value. But it says nothing about rate limits, timeouts, JS rendering behavior, robots.txt handling, or how deep-crawl failures surface for a tool whose whole purpose is deep crawling.

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 Trigger/Input/Output labelling front-loads the decision-relevant information and keeps the whole definition to a handful of lines with almost no filler. The only mild redundancy is restating the input parameters that the schema already carries.

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 steps up to describe the return value ('clean Markdown with word and token counts') and covers both inputs plus the cost model, which is enough for a two-parameter tool. It stops short of explaining crawl depth semantics for maxPages or failure/partial-success behaviour.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does add one genuinely new fact beyond the schema — maxPages defaults to 1 — but it also declares 'url' required while the schema lists zero required parameters, and it labels maxPages an integer while the schema types it as number, leaving a minor mismatch rather than extra clarity.

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

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

Names a specific verb+resource ('Deep Website to Markdown Crawler') and states the output artifact (clean, ad-free Markdown for LLM synthesis and RAG), which is concrete and actionable. It does not, however, differentiate itself from near-neighbours in the sibling list such as smartscrape_ai, so an agent cannot tell from the text alone which of the two to pick.

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 explicit 'Trigger' clause gives a clear when-to-use condition: scraping/crawling full web pages, documentation sites, or sitemaps into Markdown for LLM/RAG. There is no when-not guidance and no alternative tools named, so routing versus smartscrape_ai or the site-specific scrapers 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 it does disclose a real behavioral trait: the x402 M2M cost of 0.002 USDC on Base, which an agent must know before invoking. It also enumerates the response fields. It stops short of stating idempotency/rate limits, but validation is inherently a side-effect-free read, so the gap is small.

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

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

Front-loaded with the capability, then cleanly segmented into Trigger/Input/Output/Cost. Every sentence carries information, though the label-heavy template reads slightly boilerplate.

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 one-parameter tool with no output schema, the description correctly supplies the return contract (validity, country code, carrier, line type) and the payment cost. Nothing essential to correct invocation 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 the single 'phone' parameter is already documented with an example ('+14155552671'), so baseline is 3. The description reinforces the international/E.164 intent but adds no format constraints or validation rules beyond the schema.

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 set ('validates international phone numbers', 'carrier lookup', 'formats to E.164'), naming exactly what the tool returns. No sibling covers phone validation, so the tool is unmistakably distinct from the scraping/enrichment tools in the sibling 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?

The explicit 'Trigger: Call this tool when...' line gives clear deployment context (validating, checking validity, formatting, carrier/VoIP detection). No alternatives or exclusions are named, but no sibling is competing for this task, so the omission is minor.

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 real behavioral context — the output shape and the x402 M2M cost of 0.005 USDC on Base, which signals payment is required. However, it omits rate limits, pagination, auth prerequisites, and error behavior for an external scraping endpoint, leaving meaningful gaps.

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 Trigger/Input/Output/Cost labelling is front-loaded and scannable, with no filler sentences. It is slightly formulaic in its templated phrasing, but every clause conveys actionable information.

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 correctly enumerates the returned fields, and it discloses the payment cost. For a simple two-parameter read tool this is close to sufficient; only the mechanics of the x402 payment and any retry/auth expectations are left unexplained.

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 baseline is 3. The description adds value beyond the schema by specifying that 'limit' defaults to 20 (the schema only gives 'e.g. 20') and by clarifying that 'subreddit' is a subreddit name, which is more precise than the generic schema text.

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 (Reddit) and function (community & sentiment scraping), and details the artifacts returned (titles, authors, upvotes, comment counts). Among the many sibling scrapers, the Reddit scope makes it immediately distinguishable from instagram_scraper, tiktok_scraper, and linkedin_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?

The 'Trigger' sentence gives explicit when-to-use conditions: community discussions, sentiment analysis, product feedback, user complaints. It does not name when to prefer a sibling (e.g., review_analyzer or google_search) or state exclusions, so it stops short of a 5.

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

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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses output shape (positive/negative sentiment percentages, categorized attributes) and a per-call x402 cost of 0.005 USDC, but says nothing about error behavior, rate limits, or whether the operation is read-only.

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 labeled Trigger/Input/Output/cost sections that map cleanly to what an agent needs. Slightly formulaic and repetitive between the title-style header and the body, but no wasted 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?

No output schema exists, and the description compensates by naming the return contents (sentiment percentages and categorized product attributes). Combined with input format and cost disclosure, it covers what an agent needs for this single-parameter analysis 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 coverage is already 100%, so baseline is 3. The description adds value by specifying that 'reviews' is an array of review text strings, which corrects the schema's odd 'type: object' declaration and clarifies the accepted input format.

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

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

States a specific resource and action: extracting structured sentiment breakdown and pros/cons from raw customer reviews. This is clearly distinguishable from every sibling (all scrapers/enrichers/publishers), none of which perform review sentiment analysis.

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 'Trigger:' clause gives an explicit when-to-use condition ('structured sentiment breakdown and feature pros/cons extracted from raw customer feedback'). It lacks explicit when-not guidance or named alternatives, but no sibling competes for this task.

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. It usefully discloses the x402 M2M cost model (0.01 USDC on Base) and lists returned fields, but omits rate limits, auth/payment prerequisites, pagination, and what happens when no results are found.

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

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

Front-loads the identity, then uses labeled Trigger/Input/Output sections with no filler. The parenthetical cost note is slightly awkward but earns its place as decision-relevant information.

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 does supply the return fields (titles, prices, ratings, sold counts). It is missing some coverage for a scraper — pagination, currency/region control, and how 'buyer reviews' mentioned in the trigger actually surface in the output.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning beyond the placeholder schema text ('Input parameter for limit') by stating that limit has a default of 20. Keyword semantics are still only implied by the example.

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+resource: product search results and buyer reviews from Southeast Asian Shopee marketplaces. The regional/marketplace scoping clearly distinguishes it from siblings like amazon_bestsellers, tiktok_scraper, and ecommerce_price_tracker.

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 explicit 'Trigger: Call this tool when an agent needs...' sentence gives clear context for use, and the Shopee/SEA scope implicitly rules out other scrapers. However, it never names an alternative tool or states a when-not condition.

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, so the description carries full behavioral burden. It usefully adds the x402 M2M cost (0.01 USDC on Base) and the return shape ('strictly typed JSON matching requested schema'), but says nothing about failure behavior, timeouts, JS-rendered pages, or rate limits. Given zero annotation coverage, this is a partial disclosure.

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

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

Front-loaded with the tool name and a Trigger/Input/Output structure that is easy to scan; the only waste is repeating the tool name as a title-like prefix. Otherwise tight and well organized.

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 must cover returns and behavior; it does explain the return (schema-matched typed JSON) and cost, but omits error/failure semantics and the odd fact that both inputs are marked required:0 while the trigger implies a url and schema are needed.

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 baseline is 3. The description adds meaning beyond the schema's generic 'Input parameter for url/schema' text by clarifying that 'schema' is a JSON schema 'defining desired fields' and that output is typed to match it, so a 4 is warranted.

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: 'custom JSON extraction from an arbitrary webpage using AI schema parsing'. The word 'arbitrary' implicitly separates it from the site-specific siblings (amazon_bestsellers, linkedin_scraper, etc.), but no sibling is named, so it stops short of a 5.

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

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

The labeled 'Trigger' clause gives an explicit condition: call when custom JSON extraction from an arbitrary webpage via AI schema parsing is needed. It does not state when NOT to use it or name the site-specific scrape alternatives an agent should prefer instead.

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 does disclose the practical cost model ('x402 M2M Cost: 0.008 USDC on Base') and the shape of returned data, which is genuinely beyond structured fields. However it says nothing about auth requirements, rate limits, pagination, or error behavior for a scraping operation.

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 Trigger/Input/Output labeling front-loads the decision-relevant information and every sentence carries content. The restart of the name in the opening clause is lightly redundant, but nothing else 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 two-parameter scraper with no annotations and no output schema, the description supplies trigger, both inputs with a default, the returned fields, and the billing model. Only edge behaviors (pagination, failures, whether 'tag' is truly optional given 0 required params) remain unaddressed.

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

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

Schema coverage is 100%, so baseline is 3, but the description adds meaning: 'tag' is clarified as a hashtag keyword and 'limit' carries a stated default of 15 that the schema's placeholder example does not establish as a default. That is a real increment over the schema.

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

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

The description uses a specific verb+resource ('TikTok Trends & Video Scraper') and enumerates the data harvested (viral trends, hashtag metrics, creator engagement stats). The platform is unambiguous, which inherently separates it from the other platform scrapers (instagram_scraper, linkedin_scraper, reddit_scraper), though it doesn't name a sibling 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 'Trigger:' clause gives a clear use condition — when the agent needs TikTok trends, hashtag metrics, or engagement stats. There is no when-not guidance and no explicit alternative tool mentioned for overlapping needs, but the intended context is easy to infer.

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 disclosure burden. It usefully surfaces that this is a paid x402 M2M tool (0.008 USDC on Base) and describes the return payload, but omits rate limits, pagination, error behavior, and what happens when a query yields no results.

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

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

Front-loaded with a purpose statement and then cleanly labeled Trigger/Input/Output sections, with the cost parenthetical at the end. Dense but efficient; no filler sentences.

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

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

For a simple two-parameter scraper with no output schema, the description covers trigger, inputs, and the shape of the return payload, and flags the payment requirement. What remains missing is mostly pagination/limit interaction and failure behavior.

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

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

Schema description coverage is 100%, so the baseline is 3. The description restates 'query' (search string) and 'limit' (integer, default 10), adding only the default value of 10, which the schema does not state — a marginal gain over structured data.

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 (scrape YouTube Shorts) plus the concrete data it returns (transcripts, metadata, view counts, audio). The 'short-form YouTube videos' scope distinguishes it from the other platform scrapers (tiktok_scraper, instagram_scraper), though it never names 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 'Trigger:' sentence gives clear when-to-use context: when an agent needs transcripts/metadata/view counts from short-form YouTube videos. It does not, however, state when NOT to use it or point to a specific alternative sibling for other platforms or long-form video.

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