The Data Collector

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 posts are public, requires no auth, and returns post text, author handle, and engagement counts. This is useful behavioral context beyond the schema, though it omits any rate limits or pagination details, which would push it higher.

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

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

The description is two sentences, front-loaded with the core purpose, and each sentence earns its place. The first sentence states the action and scope; the second provides return details and an alternative. No wasted words.

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 search tool, the description is complete: it explains the resource, scope, cost/auth requirements, return fields, and an alternative for bulk use. The schema covers parameters, and there is no output schema required. Minor omissions (e.g., pagination, sorting) do not detract significantly.

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

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

The input schema covers both parameters ('query' and 'limit') with descriptions, achieving 100% schema coverage. The tool description does not add any parameter-specific semantics beyond what the schema already provides, so the baseline score of 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 clearly states the action ('Search public Bluesky posts by keyword') with a specific resource (Bluesky posts). It also distinguishes this tool from siblings by platform and mentions an alternative for bulk extraction, making the purpose unmistakable.

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

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

The description provides explicit usage context: it is free, requires no token, and targets public posts. It also explicitly directs users to a different tool for bulk profile/feed extraction, effectively stating when not to use this tool. This is 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 provided, the description carries the full burden of behavioral disclosure. It adds useful context by stating 'Free, no token required' and listing return fields (title, url, points, author, comment count). However, it does not disclose potential limitations such as rate limits, result sorting, or whether the operation is strictly read-only (though implied by 'search'). This is adequate but lacks richer behavioral details.

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

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

The description is two sentences and front-loaded with the primary action. It efficiently conveys purpose, return fields, and authentication requirements without extraneous detail. The inclusion of the provider link is minor and does not detract from overall conciseness.

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

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

Given the tool's simplicity (two parameters, no output schema), the description is largely complete. It explains what the tool does, what it returns, and the fact that no token is required. The schema covers parameter details, so the description doesn't need to re-explain them. It slightly misses behavioral notes like pagination or sorting, but overall it is sufficient for an agent to invoke the tool correctly.

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

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

Schema description coverage is 100%, as both 'query' and 'limit' have descriptions in the input schema. The description does not add additional meaning beyond what the schema already provides (e.g., it mentions 'keyword' but the schema already has 'Search keywords' for query). Therefore, it meets the baseline but does not exceed it.

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

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

The description clearly states the tool's function: 'Search Hacker News stories and comments by keyword.' It uses a specific verb ('search') and resource ('Hacker News'), and distinguishes from sibling tools like substack_search and bluesky_search by explicitly naming Hacker News. The provided return fields further clarify the tool's purpose.

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

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

The description gives clear context for when to use the tool (searching Hacker News) and even notes that it is 'Free, no token required.' While it doesn't explicitly mention alternatives or exclusions (e.g., 'use substack_search for Substack'), the platform-specific naming and the free/no-token emphasis provide sufficient guidance for a typical agent to select this tool over 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 present, so the description carries the transparency burden. It successfully discloses that the operation is free, requires no token, and returns per-result price and Store URL. While it doesn't explicitly say 'read-only', the verb 'List' implies a non-mutating operation, and the description adds meaningful behavioral context beyond 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 a single, well-structured sentence that front-loads the core purpose and immediately provides scope, contents, pricing, and workflow. Every clause adds value, with no 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?

Despite no output schema, the description specifies exactly what the returned catalogue includes (what each scraper does, per-result price, Store URL). It also covers auth requirements, cost, and a clear next step, making it sufficiently complete for a list/discovery tool with simple optional parameters.

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 only 50%: 'query' is explained, but 'limit' is not. The description does not compensate by explaining either parameter, leaving the limit parameter's behavior and default uncertain. This is a clear gap in parameter semantics.

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 ('List') and resource ('Web Data Labs scraper catalogue on the Apify Store') with concrete details about contents (50+ scrapers, pricing, URLs). It clearly distinguishes this discovery tool from its sibling 'run_scraper' by framing the workflow.

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 guidance is provided: 'Call this first to find the right actor, then run it with run_scraper.' This tells the agent when to use the tool and what to do after, making the intended workflow unambiguous relative to 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 full responsibility for disclosing side effects. It explicitly states: 'this runs on YOUR OWN Apify account using the apifyToken you pass -- the data and the billing are yours, and we never run it on our account.' This is a critical behavioral trait beyond the schema. However, it omits details about error handling, rate limits, or output structure, which prevents a higher score.

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 four sentences, each earning its place: purpose, account/billing warning, token acquisition link, and prerequisite step. It is front-loaded with the primary verb and resource, with no fluff or repetition of schema details.

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?

Despite lacking an output schema and having a nested input object, the description covers the key workflow: finding the actor via list_scrapers, providing the token, and understanding billing implications. It doesn't explain what happens with maxItems or error cases, but the schema covers maxItems and the description directs users to the actor's Store page for input schema. Overall, it is sufficiently complete for an agent to 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 baseline is 3. The description adds minimal parameter meaning beyond the schema, such as implying the actor name comes from list_scrapers, but the schema already describes each parameter. No extra semantic detail is provided for input or maxItems, so the score remains at the baseline.

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

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

The description clearly states the tool's function: 'Run a Web Data Labs scraper on the Apify platform and return the dataset items.' It specifies the verb (run), resource (scraper), platform (Apify), and output (dataset items). It also distinguishes itself from sibling tools by directing users to list_scrapers first, establishing a complementary relationship.

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

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

The description provides explicit usage context: 'Use list_scrapers first to find the actor name and its input fields.' It also warns that execution happens on the user's own Apify account, which is important for when to use (or avoid) the tool. While it doesn't explicitly mention alternatives to exclude, the guidance is 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 provided, the description carries the burden of disclosing behavioral context. It states 'Free, no token required,' which is useful auth information, and lists the returned fields (title, subtitle, publication, url, reaction count), filling some gap left by the missing output schema. However, it does not mention rate limits, pagination, result ordering, or potential side effects, so it is only partially transparent.

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

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

The description is three concise sentences, immediately stating the core purpose, then adding key context (free, no token) and return fields. Every sentence provides value with no redundant or filler content. It is appropriately front-loaded with the main action.

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 search tool with two parameters and no output schema, the description covers the essential context: what it searches, auth requirements, and the structure of results. It omits secondary details like result ordering or error handling, but given the tool's simplicity, the description is largely complete and adequate for an agent to invoke it correctly.

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

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

The schema description coverage is 100%: both 'query' and 'limit' are described with meaningful details. The description adds little beyond the schema; it says 'Search ... by keyword,' which aligns with the schema's 'Search keywords' for query. The 'limit' parameter is sufficiently described in the schema. Therefore, the description does not significantly enhance parameter understanding, meriting the baseline score of 3.

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

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

The description clearly states 'Search Substack newsletter posts by keyword,' which identifies the verb (search), resource (Substack newsletter posts), and scope. It implicitly differentiates from sibling tools like bluesky_search and hn_search by naming the platform, but it does not explicitly mention alternatives or contrast with them.

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

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

The description provides no guidance on when to use this tool versus the sibling search tools (bluesky_search, hn_search) or any other alternatives. It does not mention prerequisites, exclusions, or typical use cases beyond the inherent purpose. It simply states what the tool does without recommending it.

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