Apify Market Intelligence

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

No annotations are provided, so the description carries the transparency burden. It discloses meaningful behavioral traits: 'Public Store API only' implies no private/auth-gated data, 'no LLM' rules out generative behavior, and the cost/payment method is explicit. It does not detail output, caching, or error behavior, but for a read-only assessment tool this is acceptable.

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, information-dense sentence that front-loads the action and scope, then appends cost and payment context. There is no redundant wording or restating of the tool name.

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 tool has six parameters, a nested idea object, and no output schema, yet the description does not describe the return value or report format. The detailLevel parameter hints at dataset output, but the agent is left without explicit output expectations. The description is adequate but not self-sufficient.

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 all six parameters with detailed descriptions, so the baseline is 3. The tool description adds little about parameters beyond 'one proposed idea,' which maps to the nested idea object. It does not explain maxResults, freshnessMinutes, or detailLevel, but those are already well-documented in 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 ('Assess') and resource ('one proposed Apify Actor idea') with a clear scope: public Store similarity, activity, quality, competition, and visible pricing evidence. It distinguishes itself from sibling tools by focusing on validating a single idea rather than finding market gaps, demand, 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 clearly signals the tool's context: it uses only the public Store API and no LLM, and it carries a $0.05/call cost, implying paid, deterministic usage. It does not explicitly name alternatives or state exclusions, but the wording ('one proposed idea') makes the primary use case clear.

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 transparency burden. It discloses key behavioral traits: 'Public Store API only, no LLM' and the cost model, which are useful. However, it does not mention return format, rate limits, or side effects, though the read-only nature is implicit.

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 short sentences, each earning its place: the first defines the purpose, the second states the method and price. There is zero redundancy or filler, making it exceptionally concise.

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 tool has 7 parameters with full schema descriptions, but no output schema. The description lacks details about the return value/format, only hinting with 'rank evidence-backed market gaps.' Given the tool's moderate complexity, an explanation of output structure would improve completeness, but the core behavior is covered.

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 provides 100% description coverage for all 7 parameters, so the baseline for this dimension is 3. The description itself adds no parameter-specific details beyond what the schema already explains, so it neither enhances nor detracts from 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 clearly states the function: 'Compare user-declared niches against the public Apify Store and rank evidence-backed market gaps.' The verb 'compare' and specific resource 'Apify Store' make the purpose concrete and distinguish it from sibling tools like apify-niche-demand-radar or apify-actor-idea-validator.

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 context through 'Public Store API only, no LLM' and pricing, but does not explicitly state when to use this tool vs alternatives or provide exclusion criteria. There is no naming of sibling tools or clear 'use this when' 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 provided, the description carries the burden. It discloses the data source (public Store API), the absence of LLM, the cost, and payment method (x402/USDC on base), which is useful. However, it does not explicitly state side effects or safety (e.g., it being read-only), rate limits, or any caveats about data freshness beyond what the schema implies.

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 concise sentences. It front-loads the primary action and scope, then adds key operational constraints (public API, no LLM) and pricing. There is no redundant or filler 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?

Although the input schema is well documented, there is no output schema and the description only vaguely describes the output as a 'demand-direction snapshot'. It does not explain what fields or format to expect, nor how the different 'detailLevel' or comparison windows affect results. Given the complexity of 8 parameters and 3 enums, more detail about the output would improve 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?

Schema description coverage is 100% for all 8 parameters, so the schema already explains each field. The description adds no extra parameter-level semantics, such as how 'niche' and 'keywords' interact or what 'comparisonWindow' affects. It matches the baseline of 3 for high schema coverage.

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 produces a 'current demand-direction snapshot' for a declared niche using public Apify Store metrics. It identifies a specific verb, resource, and scope, but does not explicitly contrast with sibling tools such as apify-market-gap-finder or apify-actor-idea-validator.

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 it is used for obtaining demand-direction for a single niche, but provides no explicit conditions for when to use this tool versus siblings, nor any exclusions or alternative recommendations. It does note constraints like 'Public Store API only' and 'no LLM', but not usage context.

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

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

With no annotations, the description carries the behavioral disclosure burden. It does disclose two key behaviors: exclusive use of the Public Store API for candidate search and no portfolio fetching, plus the cost ($0.15/call, x402). However, it does not describe the output format, pagination, error behavior, or rate limits, leaving some important transparency 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 description is a single sentence plus a dash-separated clause, front-loading the primary purpose with no filler. It efficiently covers data source, cost, and scope in two concise clauses.

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 having 7 parameters, no output schema, and no annotations, the description covers purpose, data source, and cost reasonably well. However, it does not describe the structure or content of the ranked output, and it lacks comparative usage guidance for the sibling tools, so it is not fully complete for a planner tool of this complexity.

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 input schema fully documents all parameters. The description adds no parameter-specific details beyond implicitly mapping 'candidate niches' to candidateQueries and 'portfolio' to portfolioActors, which is already evident from the schema. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

Description opens with 'Rank user-supplied candidate niches by public Store opportunity and textual adjacency to a user-supplied portfolio,' clearly stating the verb (rank), resource (candidate niches), and ranking criteria. This specific purpose distinguishes it from siblings like apify-actor-idea-validator (validates ideas) and apify-market-gap-finder (finds gaps).

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 prerequisites: candidates and portfolio are user-supplied, and it clarifies that only the Public Store API is used for candidates and that no portfolio fetching occurs, setting expectations for inputs. However, it does not explicitly reference sibling tools or state when not to use this tool, so comparative guidance is absent.

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 but only discloses data source ('Public Store API only'), non-LLM operation, and cost/payment details. It does not mention behavior like caching, rate limits, or what 'summarize' produces, leaving notable gaps. The provided details are useful but not sufficient for rich transparency.

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 only two sentences and front-loads the core purpose. The billing detail ('$0.05/call, x402 (USDC on base)') is succinctly appended without bloating the text. No wasted words; concise and well-structured.

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 tool has 8 parameters, 3 enums, and no output schema, yet the description only gives a high-level summary. It does not explain what the output looks like (e.g., format, structure), how 'summarize' returns results, or how to interpret the distribution. This is a significant gap for an agent to correctly invoke and use the 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%, and every parameter already has a description (e.g., query 'Store cohort query', maxResults 'Maximum nested samples'). The description adds no extra parameter-level meaning, but the schema is already sufficient, 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 description clearly states the tool's action ('Summarize') and resource ('Apify Store event-price distributions') with a scope qualifier ('for a declared cohort'). It adds specificity with 'visible public', which helps differentiate from internal data sources. However, it does not explicitly distinguish itself from the sibling 'pricing_info' tool, preventing a perfect score.

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?

There is no explicit guidance on when to use this tool versus the sibling tools (e.g., pricing_info, apify-market-gap-finder). The description implies a use case for benchmarking pricing cohorts but never states conditions for selection or exclusions, leaving the agent to infer applicability.

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 full burden. It discloses useful behavioral context: the tool is free, returns specific data (price, payTo, network), and is intended as a first step. This goes beyond a simple listing statement.

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

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

Two concise sentences, front-loaded with 'Free' and immediately stating the action and data returned. Every word earns its place with no redundancy.

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

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

For a zero-parameter, read-only listing tool with no output schema, the description fully covers what, when, and why. It even hints at the order of operations ('first') relative to wallet readiness.

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?

Tool has 0 parameters, and schema coverage is 100% (vacuously). Baseline for 0 params is 4, and the description adds no parameter-specific information because none are needed.

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

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

The description clearly states the tool lists every paid tool in the apify-market-intelligence bundle with price, payTo address, and network. This distinguishes it from sibling tools that focus on other market intelligence aspects.

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?

Provides an explicit usage condition: 'Call this first if you don't have a wallet ready yet.' This gives clear context for when to use it, though it does not mention alternatives or when not to use it.

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