ScrapeSelect

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

No annotations are provided, so the description bears the full burden of behavioral disclosure. It does disclose the kind of output (cost range, plan, capabilities, limits, caveats), suggesting a read-only comparison operation. However, it does not mention side effects, permission requirements, rate limits, error behavior, or how workload assumptions affect results, leaving a moderate transparency gap.

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

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

The description is a single, compact sentence that front-loads the core action and scope, then efficiently lists the output aspects. Every phrase adds information and there is 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?

An output schema exists, so return values are covered elsewhere. The workload object is complex and the description does not guide users on filling it or route them to sibling tools when appropriate, but the schema's own description covers fallback assumptions. Overall adequate with noticeable gaps in usage guidance.

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%; products has a description and the Workload type has a definition-level description, but the workload parameter itself is not explained at property level. The tool description adds only 'one workload' without elaborating on its fields or how they influence comparison, so it does not sufficiently compensate for the low 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 a specific verb ('compare') and resource ('up to three products') scoped to 'one workload', and enumerates the output dimensions (monthly cost range, chosen plan, capabilities, limits, caveats). This naturally distinguishes it from siblings like estimate_cost, which implies single-product estimation, even 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 description implies its use case: comparing multiple products under a single workload. However, it gives no explicit 'when to use vs alternatives' guidance, no exclusions, and no pointer to estimate_cost for single-product estimates or search_providers for discovery. The context is present only by implication.

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 behavioral disclosure. It explicitly lists the output dimensions (low/expected/high cost), the cheapest fitting plan, rejected plans, and every assumption used, giving a clear picture of what the tool returns. No side effects are mentioned, but for an estimation tool this is reasonably 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?

A single dense sentence that front-loads the core purpose ('Monthly cost') and then enumerates the key output attributes without any filler. Every phrase 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?

Given the rich input schema and the presence of an output schema, the description provides enough context for an agent to understand what the tool does and what result to expect. It could be slightly more complete by clarifying how it relates to sibling tools, but that gap is already captured under usage guidelines.

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 does not explain individual parameters, leaving that to the input schema. The schema itself provides descriptions for 'products' and the Workload object, with extensive property titles and defaults. The phrase 'every assumption used' hints at the role of unset workload fields but adds limited parameter-specific meaning.

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 identifies the tool's action: estimating monthly cost (low/expected/high) of a workload on each product, and details what the result includes (cheapest plan, rejected plans, assumptions). It does not explicitly differentiate from sibling 'compare_products', but the cost-estimation focus is 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?

Usage is implied by the name and description: call this when you need a cost estimate for a workload. However, it does not state when to prefer it over 'compare_products' or 'recommend_scraping_stack', nor does it provide any exclusions or prerequisites.

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 must carry the behavioral transparency burden. It does disclose the type of information returned and even notes the official source of each fact, which is useful. But it does not mention read-only behavior, authentication needs, rate limiting, pagination, or error conditions.

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 compact and contains no filler, with the primary subject and conditional behavior packed into one sentence. It is slightly awkward and lacks a strong verb like 'Returns' or 'Gets', but it is efficient and readable.

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 an output schema available and both parameters documented, the description does not need to restate return shapes. Still, it leaves out practical context such as how to discover provider slugs, whether product slugs must belong to the provider, and how this tool fits with the sibling tools.

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 already documents both parameters with 100% coverage, so the baseline is 3. The description adds a little meaning by clarifying that supplying `product` unlocks plans, rates, limits, capabilities, and price history, but it does not provide additional format, constraints, or validation details.

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 clear conditional purpose: fetch a provider's products and sources, or when a product slug is given, that product's plans, rates, limits, and related details. It is distinct from search_providers because it is a details/get operation rather than a search. It loses a point for being a fragment with no explicit verb and no mention of how it relates to sibling detail tools.

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 that providing the optional `product` parameter yields fuller product-level details, which is useful. However, it gives no guidance on when to use this tool versus search_providers, compare_products, estimate_cost, or get_recent_changes, and no hint that search_providers should be used first to find the provider slug.

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 behavioral disclosure. It does add value by saying the data is 'confirmed against official pages' and 'newest first', but it does not mention whether this triggers live network fetches, whether there are rate limits or caching implications, or confirm the read-only nature explicitly.

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 phrase that front-loads the core subject and includes the most important qualifiers. There is no redundant wording or repetition of information already present in the schema.

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

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

For a simple list-like tool with three optional, well-described parameters and an output schema, the description provides enough context about the result set and its ordering. It lacks an explicit usage boundary versus siblings, but the parameter schema and output schema fill most operational gaps.

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 already documents `since` and `provider` with descriptions, and `limit` is self-explanatory with clear constraints. The description itself adds no parameter-specific meaning, but it does not need to compensate heavily because the schema coverage is adequate for these three optional parameters.

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

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

The description states a specific resource ('recent changes'), a clear domain ('price, plan and limit'), and a distinguishing trait ('confirmed against official pages'). It also specifies ordering ('newest first'), making the tool's purpose concrete and easily separable from siblings like compare_products or estimate_cost.

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 no explicit guidance on when to use this tool versus the sibling alternatives. There are no usage conditions, exclusions, or mention of alternatives. The purpose is clear, but the 'when-to-use' decision is left entirely to inference.

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

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

No annotations are provided, so the description bears the full burden. It reveals what the tool returns (fit scores, cost ranges, evidence URLs, etc.) and notes API parity via 'Same input as POST /api/v1/recommend'. It does not explicitly confirm the non-mutating nature of the operation or any external dependencies, but 'Recommend' strongly implies a read-only analysis.

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 front-loads the core purpose, lists the key output components, and adds the API parity hint. Every part earns its place with no redundant wording.

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 presence of an output schema covers the return value structure, and the description covers the high-level recommendation task. But with four parameters and sibling tools available, the description lacks guidance on when to choose this over related tools and provides almost no parameter semantics, leaving the agent to infer important invocation details from names and the schema alone.

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 25%, so the description must compensate for the undocumented parameters. It does not explain budget_usd, requires_login_session, or team_maintenance_capability, and only loosely implies that 'workload' is the main input. The reference to the API endpoint is a helpful pointer but not self-contained parameter guidance.

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 ('Recommend'), the deliverable ('an architecture and up to three providers'), the input ('for a workload'), and the contents of the result ('fit scores, cost ranges, reasons, limitations, assumptions and evidence URLs'). This distinguishes it from siblings like estimate_cost or compare_products, which focus on narrower tasks.

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 phrase 'for a workload' establishes a clear usage context: agents should call this when they need a full recommendation rather than a single cost estimate or provider comparison. However, it does not explicitly mention alternatives or state when not to use it, so some inference is required.

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. 'Find' clearly signals a read-only search and the listed categories add useful query constraints. However, it does not disclose empty-query behavior, matching semantics, or other search traits; those details live in 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 one front-loaded sentence that states the action, resource, and valid categories in order. There is no filler, and the parenthetical list of categories is compact and directly useful.

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 one optional parameter, full schema coverage, and an output schema, the description is largely complete. The only notable gap is explicit usage routing against sibling tools, already penalized under usage guidelines.

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 single 'query' parameter has 100% schema description coverage, restating the same name/product/category semantics and adding 'empty lists all'. The tool description adds no parameter meaning beyond the schema, so the schema carries the load.

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 the specific verb 'Find', names the resource ('scraping providers and products'), and scopes the search by name or category with enumerated domain values. The purpose is unmistakable, but it does not explicitly differentiate this tool from siblings like get_provider or recommend_scraping_stack.

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 the tool is for discovery by query, but it provides no explicit guidance about when to choose this tool over get_provider, compare_products, or recommend_scraping_stack. Without naming alternatives or exclusions, the agent must infer usage context from the sibling list.

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