tasas

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses that the ranking is 'EN VIVO' (live), filters to SBS-supervised houses only, and describes the output: top houses with price, and if a monto is provided, the amount received per house and the savings between best and worst. This is substantial context, though it could have mentioned potential failure modes or data freshness caveats.

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 concise sentences: front-loaded with the core purpose, then usage guidance, then output details. Every sentence adds value with no redundancy or filler. It is well-structured and appropriately sized for the tool's complexity.

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 no output schema and no annotations, the description fully explains the tool's scope, output format (top houses with price, and conditional amount/savings details), and usage triggers. It even names the data source. This is complete for a simple ranking tool with three 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 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining the behavioral effect of the monto parameter (it calculates the amount received and the savings between best and worst). It also implicitly connects operacion to buying/selling dollars. This extra context justifies 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 provides a live ranking of SBS-supervised virtual exchange houses in Peru for buying or selling dollars. It specifies the resource (casas de cambio virtuales del Perú) and the action (ranking), and explicitly differentiates itself from the sibling tool by highlighting the live, comparative nature and the source (comparadolar.pe).

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 explicitly stated: 'Úsala cuando pregunten dónde cambiar dólares, cuál es la mejor tasa hoy, o cuánto recibirían por un monto.' This provides clear context for when to invoke the tool. However, it does not mention any exclusions or explicitly name alternatives like referencias_dolar, so it falls short of a 5.

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 full burden. It discloses the three specific rate types, clarifies that these are not exchange houses, and explains the intended purpose (contextualization). This adequately sets expectations for a read-only data tool, though it omits details like update frequency or output 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 two concise sentences. It front-loads the core purpose, enumerates rate types, clarifies a boundary, and states usage context. Every clause carries meaningful information 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 tool with no output schema, the description covers the essential aspects: what it provides (three reference rates of the day), what it is not (exchange houses), and how to use it (compared against mejor_tasa). It could specify the output structure, but the simplicity of the tool makes the description reasonably 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 tool has zero parameters, so per the rubric the baseline is 4. The description compensates by explaining the content of the result (rate types) and their purpose, adding value beyond the empty 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 tool returns reference exchange rates for the day in Peru, explicitly listing the types (official SBS/SUNAT, parallel/informal, USDT P2P). It also distinguishes itself from sibling tool mejor_tasa by clarifying it provides contextual data, not actual exchange house rates.

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 provides usage context: the rates 'sirven para contextualizar y comparar contra el ranking de mejor_tasa' – they are for context/comparison, not for actual exchange. It also explicitly negates 'NO son casas de cambio', which tells the agent when not to use it and implies mejor_tasa as the alternative for actual rates.

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