Apify Market Intelligence

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

The description adds valuable behavioral context beyond annotations: it discloses the cost ('$0.05/call, x402 (USDC on base)') and notes that it uses only the Public Store API with no LLM, implying deterministic evidence-based behavior. Annotations already indicate non-read-only and non-idempotent behavior, which is not contradicted. It does not detail all side effects, but the added cost/source context is useful.

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, front-loaded sentence that states the core purpose and then adds crucial operational constraints (API-only, no LLM, cost). It contains no filler and is highly readable for an agent.

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 the description does not explain the return value or report format beyond the assessment concept. Some clues exist in parameter descriptions (e.g., detailLevel mentions 'output in the dataset row'), but the description itself is thin on output details. Given the moderate complexity, it is adequate but could be more 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 parameters are well documented in the schema. The description does not add parameter-specific details beyond repeating the idea assessment concept. It mentions cost but not per-parameter semantics. Baseline of 3 is appropriate since the schema carries the parameter documentation.

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

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

The description clearly states the action ('Assess one proposed Apify Actor idea') and the resource/scope ('against public Store similarity, activity, quality, competition and visible pricing evidence'). It distinguishes the tool from siblings by emphasizing single-idea validation, which contrasts with market-gap-finder or niche-demand-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?

The usage is implied through the purpose: this is for validating an individual idea. However, there is no explicit 'when to use' or 'when not to use' guidance, and no alternatives are mentioned. The phrase 'Public Store API only, no LLM' hints at constraints but does not specify how it should be selected relative to sibling tools.

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

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

Annotations already provide readOnlyHint=false, openWorldHint=true, idempotentHint=false, and destructiveHint=false. The description adds valuable context: it uses only the public Store API (no LLM) and explicitly states the cost/payment method ($0.10/call, x402 USDC on base), which helps the agent understand external API calls and potential charges. No contradiction with annotations.

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

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

The description is two sentences, front-loaded with the core purpose, and includes cost/payment details without wasting words. Every sentence adds value.

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

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

The description covers the high-level purpose and key constraints (public API, no LLM, cost). However, there is no output schema, and the description does not specify the return format or structure of the 'evidence-backed market gaps' report. For a 7-parameter tool, this leaves some ambiguity about expected outputs and how results are delivered.

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%, with each parameter (requestId, seedQueries, categories, maxResults, detailLevel, schemaVersion, freshnessMinutes) having its own description in the schema. The tool description adds no additional parameter-level detail, so the baseline score 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?

The description uses specific verbs ('Compare', 'rank') and clearly identifies the resource ('user-declared niches against the public Apify Store'). It clearly differentiates from sibling tools by focusing on evidence-backed market gaps, which is a distinct objective from idea validation, demand radar, or pricing benchmarks.

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

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

The description implies a use case: when you have niche queries and want to find market gaps in the Apify Store. However, it does not explicitly state when to use this tool versus siblings like apify-niche-demand-radar or apify-actor-idea-validator, nor does it provide exclusion criteria.

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

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

The description adds behavioral context beyond annotations by stating 'Public Store API only, no LLM' and disclosing the cost and payment method ('$0.05/call, x402 (USDC on base)'). These are useful details not present in the annotations, which only provide generic 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?

The description is extremely concise, with two sentences that front-load the purpose followed by key constraints. Every word earns its place; there is no verbose 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?

There is no output schema, and the description does not clarify what the 'snapshot' looks like in terms of structure or content. The tool has 8 parameters with rich schema descriptions, but the overall behavior and result format remain somewhat vague, making it less complete for an agent needing to understand the full context.

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

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

The input schema has 100% coverage with descriptions for every parameter, so the baseline is 3. The description does not add parameter-specific details beyond what the schema already provides, nor does it clarify any parameters that the schema leaves ambiguous.

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 'Produce' and clearly identifies the resource: 'a current demand-direction snapshot for one declared niche'. It also differentiates from sibling tools by specifying the method ('using observed public Apify Store user metrics') and constraints ('Public Store API only, no LLM').

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

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

The description clearly implies when to use this tool: when a demand-direction snapshot for a single niche is needed. It provides context about the data source and cost but does not explicitly mention alternatives or exclusions relative to sibling tools, though the purpose is distinct enough.

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

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

The description adds behavior not fully covered by annotations: it is a paid call ($0.15/call), it uses the Public Store API for candidates only, and it does not fetch the portfolio. This clarifies the operational context and constraints. However, side effects are not fully described, and annotations already indicate non-read-only status.

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

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

The description is a single sentence that concisely conveys functionality, constraints, and pricing. It is front-loaded with the main action and resource, and every clause carries meaningful information without unnecessary elaboration.

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

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

The description explains what the tool does and key constraints, but since there is no output schema, it does not describe the return format or how rankings are presented. Given the tool's complexity (7 parameters, including arrays and enums), more detail about output and expected behavior would be needed for full completeness.

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

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

The input schema has 100% documentation coverage for all 7 parameters, including types, defaults, constraints, and descriptions. The tool description does not add any parameter-level meaning beyond the schema, so the baseline 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 tool's purpose: it ranks candidate niches by public Store opportunity and adjacency to a user-supplied portfolio. The verb 'rank' is specific and the resource ('candidate niches') is well-defined. It distinguishes itself from sibling tools by focusing on portfolio expansion and explicitly noting it does not fetch portfolio data.

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

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

The description provides context about scope (uses Public Store API, does not fetch portfolio) but does not explicitly state when to use this tool over its siblings. No alternatives or exclusions are mentioned, leaving the decision to the agent based on the name and purpose alone.

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

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

The description adds meaningful behavioral context beyond annotations: 'Public Store API only, no LLM' reveals implementation constraints, and '$0.05/call, x402 (USDC on base)' discloses cost and payment method. This goes beyond the openWorldHint and other annotations without contradicting them.

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 remarkably succinct: a single sentence for the primary action and a dash-separated fragment for cost/technical details. There is no redundant wording; every element earns its place.

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

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

For a tool with no output schema and 8 parameters, the description covers the core action, scope, cost, and implementation constraint. It does not define the exact return format or explain the 'x402' protocol, but the presence of full parameter descriptions and clear purpose makes it mostly 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 coverage is 100% — every parameter has a description in the input schema. The tool description itself does not add any parameter-specific semantics, so the baseline 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 opens with 'Summarize visible public Apify Store event-price distributions for a declared cohort', which is a specific verb+resource+scope statement. It clearly distinguishes this tool from sibling advisors like apify-actor-idea-validator or apify-market-gap-finder by focusing on pricing benchmarks.

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

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

The description implies usage by mentioning 'declared cohort' and 'public Apify Store', but it does not explicitly state when to choose this tool over alternatives such as pricing_info. There is no mention of when-not-to-use or exclusion criteria.

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

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

Annotations already declare read-only, open-world false, and non-destructive hints. The description adds context by mentioning the tool is free, lists specific return fields (price, payTo address, network), and implicitly clarifies it provides pricing info without side effects. This goes beyond annotation basics, though it doesn't detail response format or edge cases, explaining the 4 rather than 5.

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, compact sentence that packs essential information: free, scope (all paid tools), response fields, and when to call. No unnecessary words or redundancy, making it highly efficient.

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

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

For a simple zero-parameter tool with no output schema, the description fully covers what the tool returns (price, payTo, network) and when to use it. The annotations cover safety, and the description fills in the return semantics and usage context, making it 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?

The tool has zero parameters, so the baseline is 4. The description correctly omits parameter details, instead conveying the tool's behavior, which is all that's needed when input schema is empty.

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: it lists every paid tool in the 'apify-market-intelligence' bundle with price, payTo address, and network. The verb 'list' plus the specific resource and attributes make the purpose explicit and distinguish it from sibling tools focused on ideas, gaps, demand, etc.

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 when-to-use guidance: 'Call this first if you don't have a wallet ready yet.' This indicates the tool is a prerequisite and its free nature contrasts with paid sibling tools, effectively helping the agent choose it at the right time.

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