agent

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds useful behavioral context by stating the result focuses on current sales, highest discount, and an example product. It does not disclose details like output size, whether multiple brands are returned, or link/code behavior beyond what the markt parameter description hints at.

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 concise sentence, front-loaded with the essential qualifiers: current sales, supplement brands, selected market, highest discount, and example product. There is no filler or redundant repetition 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?

There is no output schema, so the description carries the burden of explaining return values. It gives a solid gist but leaves the 'merk' parameter semantics unclear and does not describe the exact output shape or how the highest discount is determined. Given the simple tool surface, this is adequate but not fully 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 schema documents 'markt' with an enum, default, and description, and the tool description echoes both 'supplementmerken' and 'gekozen markt' to map to the parameters. However, 'merk' has no schema description, and the tool description does not clarify whether it is an optional filter, how it interacts with 'markt', or what happens when it is omitted.

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 what the tool returns: supplement brands currently on sale in the selected market, with the highest discount and an example product. Although phrased as a question rather than an imperative, it names a specific resource and scope, and 'nú' distinguishes it from historical or expected-sale 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 provides clear context: use this tool when you need current supplement-brand sales in a market, highlighting the highest discount. It does not explicitly name sibling alternatives or when not to use them, so some inference is required, but the 'current sale' framing makes the intended use reasonably obvious.

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 mark the operation as read-only and non-destructive. The description adds that the result is a ranking but does not disclose ranking criteria, number of results, or whether output includes prices/links/codes (the schema's markt description hints at these). 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?

One short sentence conveys the purpose and examples without filler. The key phrase 'ranking of the best products' is front-loaded.

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 read-only two-parameter tool, the description is mostly adequate, but with no output schema it should specify more about what the ranking contains (e.g., products, scores, prices/links/codes) and how many results are returned. The markt description in the schema covers market-specific output, but ranking behavior itself is under-specified.

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 categorie parameter has no schema description, but the text adds domain examples (eiwitten, creatine, pre-workout). The markt parameter is well documented in the schema. With 50% schema coverage, the description partially compensates but gives no exact accepted category values or formatting 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 names the resource ('ranking of best products in a category') and supplies concrete category examples, so an agent can understand what the tool returns. It stops short of an explicit verb and does not directly contrast with sibling tools like zoek_producten, but the ranking scope is reasonably distinctive.

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 examples imply the tool is for retrieving a category's top products, which is a usable hint. However, there is no explicit when-to-use/when-not-to-use guidance or mention of alternatives such as zoek_producten or product_oordeel.

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 readOnlyHint=true and destructiveHint=false, so the read-only nature is covered. The description adds useful behavioral context by disclosing that the result includes a last-verified date and a success percentage from user votes, which goes beyond the 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?

A single, front-loaded sentence that states the core resource first and then the distinguishing metadata. Every clause earns its place, and there is 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?

For a simple two-parameter read-only lookup with no output schema, the description covers the main return elements (code, verified date, success percentage) and the market context. It could mention what happens when no verified code exists, but that is a minor gap given the tool's simplicity and annotations.

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% because 'merk' has no schema description. The description clarifies that 'merk' means the brand and relates 'markt' to the chosen market, but it does not specify format, required spelling, or behavior when no code is found. It provides modest added value but does not fully compensate for the missing 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 identifies a specific resource ('kortingscode voor een merk'), a context dimension ('gekozen markt'), and distinctive output attributes (last-verified date and success percentage). This makes it clearly distinguishable from siblings like 'prijs_historie' and 'actuele_sales'.

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 retrieving a current verified discount code for a brand in a market, but it never explicitly states when to prefer this tool over alternatives. Sibling names are present in context, but the description does not reference them or provide 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?

The description adds meaningful behavioral detail beyond the readOnlyHint annotation: it states the data is daily measurements and that the tool evaluates whether a current discount is genuine. This gives the agent insight into the analysis performed, not just the fact that it is a read 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?

A single, information-dense sentence. It front-loads the core purpose and adds the distinguishing discount check without any redundant 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?

There is no output schema, so the description carries responsibility for explaining return semantics; it does so at a high level (low/high/median, discount check). However, it omits how the optional parameters like dagen and markt affect results and does not clarify the exact expected response format.

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?

With only 33% schema description coverage, the description should compensate for undocumented parameters, but it does not. product_key is not explained, and dagen is not connected to the 'dagmetingen' concept. Only markt has a schema description; the tool description adds no parameter-level 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?

The description clearly identifies the tool as providing price history for a product with daily measurements and specific statistics (low/high/median) plus a discount-authenticity check. It distinguishes this tool from sales-focused siblings by its price-history focus, though it lacks an explicit verb like 'retrieve' or 'get'.

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 context is implied: an agent would use this when price history or discount legitimacy is needed. However, there is no explicit guidance on when to prefer this tool over alternatives such as actuele_sales or week_deals, nor any when-not-to-use conditions.

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 readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds useful context about what the tool returns (SupplementScore, substantiation, reviews, current price) but does not disclose data freshness, limitations, or pricing behavior beyond 'actuele prijs'.

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 with no filler. Every element contributes meaning: independence, single-product scope, and the key output components.

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 read-only annotations, simple input schema, and absence of an output schema, the description adequately conveys the tool's return content. It is missing some guidance on alternatives and product_id semantics, but the core information needed to invoke and interpret the tool is present.

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 documents markt with its enum and default but product_id has no description. The description's 'over één product' clarifies that product_id targets a single product, adding some meaning, but it does not compensate fully for the undocumented product_id or explain how markt affects output.

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 provides an independent quality judgment for one product, including SupplementScore, substantiation, reviews, and current price. It is specific about scope and output, though it does not explicitly differentiate from siblings like beste_in_categorie or prijs_historie.

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 guidance on when to use this tool versus alternatives such as beste_in_categorie or score_methodologie. The phrase 'over één product' implies single-product use, but there are no explicit conditions, exclusions, or references 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 declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds useful behavioral context by stating the output is machine-readable and complete, and by listing the content components an agent can expect, though it stops short of specifying the exact machine-readable format.

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 object and then lists its main components. There is no filler or repetition of information already fully covered by 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 read-only tool with no output schema, the description communicates the main return contents well. It is slightly incomplete because it does not elaborate on the exact machine-readable structure or how the 'markt' parameter alters the output, but those details are at least partially covered by the parameter schema.

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 fully documents the single 'markt' parameter with an enum, default value, and a descriptive explanation, so the baseline is 3. The description adds no further meaning about how the market choice affects the returned methodology.

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 identifies the resource as the complete SupplementScore scoring model and enumerates its machine-readable contents: criteria, letter scale, burden of proof, independent review, and dispute procedure. This clearly communicates what the tool provides and distinguishes it from product/sales-oriented sibling tools, though it lacks an explicit verb such as 'returns' or 'retrieves'.

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 purpose implies this tool should be used when the scoring methodology itself is needed, as opposed to product scores, sales data, or deals. However, the description gives no explicit when-to-use or when-not-to-use guidance, nor does it reference any alternative sibling tool.

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 readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds context about the data sources (brand announcements and own sale observations) and indicates output details (period and discount), but does not disclose limitations such as data freshness or uncertainty. This is acceptable given the 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?

A single, focused sentence that front-loads the purpose ('Sale-planning vooruit') and includes the key scope and data sources. 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?

With no output schema, the description hints at what the tool returns: sales per brand with period ('van/tot') and discount ('korting'). It also explains the basis of the forecasts. While it doesn't specify response structure or pagination, this is sufficient for a simple read-only query tool with only two 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%: 'markt' is well-documented with enum and default, but 'merk' has no schema description. The tool description only repeats 'per merk' without explaining what values are valid, exact match, or required. It does not compensate for the undocumented parameter.

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 forward-looking sale planning tool ('Sale-planning vooruit') that provides announced and expected sales per brand, including period and discount. It is specific about the resource (sales) and the source (brand announcements and own observations), and it implicitly distinguishes itself from the sibling 'actuele_sales' by focusing on expected rather than actual sales.

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: this tool is for planning ahead and viewing expected/announced sales, which tells an agent when to use it. It does not explicitly name alternatives or state exclusions, but the forward-looking framing is enough to separate it from current-sale tools like 'actuele_sales'.

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 cover read-only and non-destructive behavior. The description adds useful context by specifying the result contents (old/new price and discount) and by narrowing the effective market scope to nl/be. It does not contradict the 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 a single compact sentence with no filler. Every part adds scope, output content, or a usage restriction.

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 read-only deal-list tool, the description covers the core return fields and the key usage restriction. However, there is no output schema, the winkel parameter remains unexplained, and no sibling guidance is provided, making the definition minimally adequate rather than 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 description clarifies the markt parameter by stating that the tool only works for nl/be, which is more restrictive than the schema enum. However, the winkel parameter has no schema description and is not mentioned in the description either, leaving a meaningful gap at 50% 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 identifies what the tool provides: current week-folder deals with old/new prices and discounts, restricted to nl/be. It is not a tautology and the resource is specific, but it does not explicitly differentiate from sibling tools such as actuele_sales.

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 an explicit market limitation ('alleen voor markt nl/be'), which helps the agent avoid de/fr/es despite the schema enum. However, it does not state when to prefer this tool over siblings or mention any alternative, so the usage guidance remains mostly implicit.

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 readOnlyHint and destructiveHint, so the description needs only to add scope and output behavior. It does so by specifying coverage of all shops including non-Shopify and by naming the returned data: price per 100 g/serving, SupplementScore, and current discount code. There is no contradiction with the 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 a single front-loaded sentence that states the action, scope, and key output fields with no filler. Every clause contributes useful information, and the parenthetical '(ook niet-Shopify)' adds a meaningful differentiator without extra length.

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 three-parameter tool with no output schema, the description covers the main purpose and return fields, which is helpful. However, it leaves max_prijs semantically unexplained and gives no guidance on result ordering, limits, or how to choose among siblings. It is adequate but has clear 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?

Schema description coverage is only 33%, with only markt having a description. The description does not clarify zoekterm or max_prijs; max_prijs is especially under-specified because it is never mentioned in the description, even though it is a number parameter whose filtering behavior is not obvious from the schema alone.

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 the specific verb 'Zoek' with the resource 'supplementen' and clearly defines the scope: all shops in the selected market, including non-Shopify. It also lists the key result features (price per 100 g/serving, SupplementScore, current discount code), making the tool's purpose unambiguous and distinguishable from narrower sibling 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 when to use the tool — whenever the user wants to search supplements across a market — but it does not explicitly mention alternatives or when not to use it. Sibling tools like beste_in_categorie, prijs_historie, or week_deals are not referenced, so the agent must infer the boundary.

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