Compera

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

No annotations are provided, so the description carries the behavioral disclosure burden. It discloses the main behavioral trait—returning a ranked top list—and enumerates the output fields. It does not explain ranking criteria or data freshness, but this is reasonable for a simple read-style lookup.

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 compact sentences with the main purpose front-loaded. Examples and output fields are packed efficiently with no filler or repetition.

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 one-parameter tool with no output schema, the description adequately covers what is returned (name, price/terms, rating, link) and how to obtain valid category values. An agent has enough information to invoke the tool and interpret its results.

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 the schema already describes the category parameter and directs callers to list_categories for valid values. The description adds illustrative examples but no extra semantic detail, which is acceptable given full 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 uses a specific verb ('Jämför') and a well-scoped resource ('leverantörer i en tjänste-/abonnemangskategori') with concrete examples. It also states the output (top list with name, price, rating, link), clearly differentiating it from siblings like compare_pair.

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 use case: comparing providers within a single category. It does not explicitly name alternatives or say when not to use it, and the only cross-reference to list_categories appears in the schema rather than the description itself.

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 carries the burden. It does disclose the return contents (price, rating, badge, and advantages for both options), but it does not mention side effects, error behavior, or whether options must already exist in the category.

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 conveys purpose, scope, example, and return values with 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 three-parameter tool, the description covers the core purpose, a concrete example, and the returned fields, which is especially useful given there is no output schema. Minor gaps remain, such as not explicitly pointing to list_categories for valid category values, but the schema already covers that.

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 schema already documents all three parameters. The description adds little beyond the schema, mostly repeating the example values already present in the parameter descriptions.

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 and resource: compare two suppliers/alternatives in a category, with a concrete example (Bahnhof vs Telia i bredband). It is distinguishable from the sibling compare_category by focusing on pairwise comparison, though it does not explicitly name or differentiate that sibling.

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?

No explicit when-to-use or when-not-to-use guidance is provided. The description does not mention compare_category or any alternative, so an agent must infer when pairwise comparison is appropriate versus other comparison 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states that it lists current offers, implying a read-only operation, but does not explicitly mention side effects, pagination, or response format. For a simple listing tool this is acceptable, but it could be more explicit about safety and output.

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 purpose and includes relevant examples. There is no redundant or filler content; it is appropriately concise for a simple tool.

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 adequately covers what the tool does for a simple listing: it specifies the platform (Compera) and provides examples of categories. With a single optional parameter fully documented in the schema, the description is sufficient for an agent to invoke the tool correctly. It could optionally mention that it returns a list of deals, but that is implied.

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 fully documents the single optional 'limit' parameter with a description (default 8, range 1-20). The description adds no additional parameter semantics, which is fine given the schema coverage is 100%.

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 current offers/discounts across categories, giving examples like electricity, mobile, streaming, and insurance. It conveys a specific verb (list) and resource (current deals), and the name 'deals_now' reinforces this. However, it does not explicitly differentiate itself from sibling tools like product_offers or compare_category, so it misses an opportunity to be fully distinct.

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 alternatives. There is no mention of scenarios where one would prefer deals_now over product_offers, compare_category, or other siblings. An agent is left to infer usage from the name and description 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?

No annotations are provided, so the description carries the burden. It discloses the data source (Nord Pool), the unit (öre/kWh), and the output highlights (cheapest/most expensive and difference). However, it does not mention whether the data is cached, how fresh 'dagsfärskt' is, or whether the tool makes an external network call. It also doesn't describe the full return structure beyond the highlights.

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 purpose (fresh Swedish electricity price), then adds the source, unit, and output highlights. Every word earns its place; no filler or repetition.

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 one optional parameter and no output schema, the description covers the essential information: what it returns, in what unit, and how the parameter affects the result. It could be more complete by stating the return format (e.g., a list or object) and data freshness, but given the tool's simplicity, the description is largely 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?

Schema coverage is 100%: the only parameter 'area' is fully described with an enum and an explanation that omitting it returns all four areas. The description adds the context that the areas are SE1–SE4 and that the result is in öre/kWh, which complements the schema. Since the schema already covers the parameter well, a baseline of 3 applies, and the description's added context about the optional behavior earns a 4.

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 provides fresh Swedish spot electricity prices per area SE1–SE4 from Nord Pool, in öre/kWh, and shows the cheapest/most expensive area and the difference. This is a specific verb+resource with clear scope, and it distinguishes itself from the sibling tools (which are about product categories, loans, and comparisons) by naming the exact data source and units.

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 this tool: when the user asks for current Swedish electricity prices by area. It also explains the optional area parameter behavior ('Utelämna för alla fyra' – omit for all four). However, it does not explicitly state when not to use it or mention alternatives among the siblings, though the siblings are clearly unrelated in domain.

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 behavioral disclosure burden. It indicates that the tool returns a list of categories and how-to-search guidance, implying a read-only informational operation. It does not go deeper into formatting or limitations, but for a simple no-parameter tool this is adequate.

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 Swedish sentence delivers both the core purpose and the secondary deliverable without wasted words. The primary action is front-loaded, and every phrase adds meaning.

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 no-parameter, no-output-schema tool, the description covers what the tool does and why an agent would call it. It could be more explicit about the exact response format, but the stated deliverable is simple enough that an agent can invoke it correctly with the current description.

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 does not need to explain parameter semantics, and the empty schema leaves nothing ambiguous for an agent to configure.

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 ('Lista') and a specific resource ('alla jämförbara tjänste-/abonnemangskategorier'), and explicitly states these are valid values for compare_category. This clearly distinguishes it from sibling tools like compare_category and search_products while also noting that it provides search guidance.

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: use this tool when you need valid values for compare_category, and also when you need instructions on how to search products. It does not explicitly name exclusions or alternative tools, but the intended use cases are evident.

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 disclosure burden; it adds useful context by naming the data source (Compera Ränteindex) and freshness ('dagsfärskt'). It does not specify response format or update behavior, but these omissions are modest for a no-parameter lookup.

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 compact sentence that front-loads the main result and appends the source and ranking detail. No 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?

For a zero-parameter tool, the description names the metric, geography, freshness, source, and the top-list component. It could mention ordering or caveats, but nothing essential is missing for invoking 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?

There are no parameters, so the schema has nothing to document. The baseline is 4, and the description adds relevant scoping context by specifying geography ('i Sverige') and output granularity ('per långivare').

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 (lowest effective private loan rates in Sweden) and adds source/freshness context ('dagsfärskt Compera Ränteindex') plus a top list per lender. It lacks an explicit action verb but is clear and distinguishable from sibling comparison 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?

There is no guidance on when to use this tool versus siblings like compare_category or product_offers. The zero-parameter signature makes it self-contained, but the description leaves selection criteria 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?

No annotations are provided, so the description carries the full burden. It discloses the output content (median/average spread plus examples), but it does not state the return format, whether the data is current or historical, or any limitations. This is adequate but leaves several behavioral details 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?

Two short sentences: the first defines the metric and output content, the second gives a concrete use case. There is no redundant or filler text.

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 information tool, the description is nearly sufficient: it states what is measured, what output to expect, and when to use it. It does not explicitly describe the response format, but with no input parameters and no output schema, the remaining gap is minor.

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 schema already covers everything; the description adds domain context by specifying 'same product' and 'Swedish stores,' which helps the agent understand the tool's scope even though no arguments are needed. The baseline of 4 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 defines price_spread as an index of how much prices for the same product vary across Swedish stores, including median/average spread and examples. This is specific and non-tautological, but it lacks an explicit verb and does not explicitly differentiate from 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 phrase 'Bra för "spelar det roll var jag handlar?"-frågor' explicitly identifies the user-intent context for using the tool. It gives clear usage context, though it does not mention when not to use it or name alternatives.

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 behavioral disclosure burden. It goes beyond a generic statement by specifying scope (ALL stores), sorting (cheapest first), and the stock filter (in stock first). While it doesn't mention response format, pagination, or auth, it gives enough behavioral context for a simple read-only fetch.

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: one sentence with the core behavior and one short instruction for obtaining the parameter. Every sentence earns its place, and the key information 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 one-parameter tool, the description covers the essential behavior, sorting logic, and parameter source. The only notable gap is the lack of explicit detail about the output structure, but given the tool's low complexity and self-explanatory purpose, this is minor.

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%, so the baseline is 3. The description adds value by explaining where to obtain the product_slug (from search_products links) and providing a URL pattern, which is not present in the schema. This helps the agent construct the parameter correctly.

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 uses a specific verb 'Hämta' (fetch) and clearly states the resource: ALL stores' prices for a single product. It also includes the sorting behavior (cheapest + in stock first), which distinguishes it from sibling tools like compare_pair or price_spread.

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 usage context: use this tool for a single product's offers across all stores that are in stock, cheapest first. It also instructs the agent to take the product_slug from search_products links, which is helpful routing guidance. It does not explicitly mention when not to use it or name alternative 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?

With no annotations, the description carries the burden of behavioral disclosure. It does disclose the core behavior: searching the catalog and returning the cheapest price, number of stores, and a product page link. However, it does not mention edge cases like empty results, sorting defaults beyond what the schema provides, or response structure.

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 short sentences, front-loaded with the main purpose and output, followed by usage context and a return-value note. Every sentence contributes useful information without 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 simple search tool with three well-documented parameters and no output schema, the description covers the essential context: what to search, what output to expect, and the link returned. It is slightly sparse on no-result behavior and response shape, but 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%, so the schema already documents all parameters including examples and defaults. The description does not add additional parameter-level meaning beyond what the schema 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 ('Sök i Comperas produktkatalog') and the resource, and specifies the output ('billigaste pris + antal butiker per produkt'). It does not explicitly name a sibling tool to differentiate from, but the scope of physical products and the output detail make the purpose clear.

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 usage context: 'Använd för fysiska produkter (TV, mobil, hörlurar, dator, vitvaror m.m.)'. This tells the agent when the tool is appropriate, though it does not explicitly state when not to use it or name alternative tools.

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