radarscore

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

The description mentions the comparison aspects (prices, availability, tech specs, RadarScore axes) and that evidence levels are provided. However, it does not detail how it handles missing data or the exact output format. No output schema is provided, reducing 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 concise and to the point$ It provides the tool's purpose and the parameter in a direct manner. The schema is minimal and clear.

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 is self-contained for usage, but lacks explicit output specification or edge-case handling. The sibling tools might provide context, but the tool itself doesn't describe how it interacts with other tools or what the output structure is. So it is somewhat incomplete.

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 parameter 'ids' is described as model keys with an example. The min and max items (2 and 4) are clear. The description is in German but that's acceptable given the bilingual tool description. No enums are provided, but the description gives an example of the key format.

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 purpose: comparing 2-4 robot models side-by-side on prices, availability, tech specs, and RadarScore axes. The bilingual note reinforces the function.

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 explains that 2-4 models are compared)Skip the note? Actually the description says 'Stellt 2–4 Modelle nebeneinander' which is the action. The input schema requires ids with minItems 2 and maxItems 4, which directly supports the usage. No explicit conditional instructions, but it is clear that the user provides model IDs.

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 content returned (version, license, URLs, etc.) and implies a read-only operation, but does not mention potential staleness, authentication, or side effects. It is adequate for a simple metadata lookup but lacks depth.

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 concise sentence in German with an English summary tail. Front-loaded with key terms, efficient, though the bilingual repetition adds slight length without additional 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 zero-parameter metadata retrieval tool with no output schema, the description covers all expected information: what is included and the stable download URLs. It is complete enough for an agent to understand its purpose and expected result.

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?

No parameters exist (0 params, schema coverage 100%), so baseline is 4. The description itself does not need to explain parameters since there are none.

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?

Provides specific verb 'metadata' and lists exact fields (version, date, license, citation, methodology link, model scores, download URLs) for the RadarScore dataset. Clearly distinguishes from sibling tools that operate on robots.

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 guidance on when to use this tool versus others. While it's a metadata resource, the description does not state that it should be used to obtain dataset context before querying data or compare with alternatives like search_robots.

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 transparency burden. It discloses the full return contents and the conditional behavior of include_belege, including the trade-off of a significantly larger response with individual citations instead of summaries. It does not mention errors, permissions, or rate limits, but for a simple read-by-id tool the behavioral disclosure is strong.

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 front-loaded, with the first sentence giving the full purpose and content inventory, and the second sentence explaining the optional parameter effect. Every sentence earns its place; the bilingual suffix is a minor redundancy but not harmful.

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 simple 2-parameter schema, no output schema, and no annotations, the description provides enough information about what the tool returns and how include_belege changes the response. It could be slightly more complete by explicitly stating the response is a record/JSON object, but the phrase 'Datensatz-Eintrag' and the content list already convey this adequately.

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 both id and include_belege in detail. The description repeats the same semantics (slug from search_robots and the effect of include_belege) without adding meaningful new meaning beyond the structured 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 this tool retrieves the full record for exactly one robot by ID, listing the specific content areas (prices, technology, RadarScore axes, autonomy status, evidence summaries, audit data). This verb+resource framing distinguishes it from siblings like search_robots, compare_robots, and dataset_info.

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 by the note that the id comes from search_robots, suggesting this tool is used after searching to fetch full details for a single model. However, it does not explicitly state when to prefer get_robot over compare_robots or dataset_info, nor does it provide any exclusions or alternative 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?

No annotations were provided, so the description carries the full burden of behavioral disclosure. It excels here: it transparently reveals the calculation behavior, including specific rates (2.7% Zoll, 19% MwSt), currency conversion logic (USD×Kurs), and the fact that modell_id fetches a documented list price. However, it doesn't address edge cases such as what happens when both parameters are provided simultaneously or what error/fallback behavior occurs for invalid inputs.

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 well-structured sentences that front-load the core purpose and formula. Every element serves a purpose: the formula, the parameter choice, and the use case. Slight redundancy exists due to the bilingual structure (German and English essentially repeat the same information), but both versions are concise and add accessibility value for the target audience.

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 pure calculation tool with no output schema and no side effects, this description is nearly complete: it covers purpose, formula, parameter selection, and geographic context. The version_eur default (250/800/1500€ by weight class) is mentioned in the schema as required. The only gap is the missing description of the return format (total amount only vs. detailed breakdown), but that is acceptable given the absence of an output 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?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful value beyond the schema: it establishes the mutual exclusivity relationship between modell_id and preis_usd (not clear from the schema alone) and ties the parameters into the overall cost formula context. It also references the weight-class-based default for versand_eur, which enriches understanding of that 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 uses a highly specific verb ('Rechnet'/'calculating') paired with a precise resource ('Import-Faustregel für den Direktimport eines Roboters'). It includes the exact formula (Warenwert + Versand + 2.7% Zoll + 19% Einfuhrumsatzsteuer), making it impossible to confuse with any sibling. It clearly distinguishes itself from compare_robots, search_robots, etc., which are about comparison and search, not cost calculation.

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 explicitly explains the either/or choice between modell_id and preis_usd ('Entweder modell_id... oder preis_usd frei angeben'), providing clear guidance on parameter selection. It clearly defines the use case (direct import from China/USA). However, it lacks explicit exclusion criteria or named alternatives (e.g., 'for trade pricing, use X').

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 mentions the output (compact rows with prices, total score, autonomy indicator) but does not disclose behavioral traits like whether the operation is read-only, any side effects, or rate limits. Since no annotations are provided, the description carries the burden but lacks explicit details about side effects or limitations.

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 concise, consisting of two sentences in both German and English. It is front-loaded with the main action ('Durchsucht den RadarScore-Datensatz') and efficiently lists the filters and output, with no unnecessary fluff.

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 provides sufficient context for a search tool: it names the dataset, lists filter criteria, and specifies the output fields. It does not explain edge cases or pagination, but given the simplicity of the tool and the presence of a detailed schema, it is largely complete. The mention of output details adds value beyond the 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 input schema already provides detailed descriptions for all six parameters, covering 100% of the schema. The tool description only summarizes these parameters (e.g., 'filters for category, DACH availability') without adding new meaning beyond what the schema states, so the baseline of 3 applies.

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

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

The description clearly states the tool's function: it searches the RadarScore dataset with filters for category, availability, origin, and price, and returns compact rows with specific fields. This distinguishes it from sibling tools like compare_robots, get_robot, or dataset_info.

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 this tool is for searching and filtering robots, which separates it from other tools like get_robot (specific robot) or compare_robots (comparison). However, it does not explicitly name alternatives or state when to prefer this tool over others, making it slightly less explicit than a perfect score.

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