Cambio Uruguay

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

With no annotations, the description carries the behavioral burden. It clearly defines what 'best' means for each side ('lowest sell price' vs 'highest buy price') and signals real-time data with 'right now.' It does not describe the return shape or edge cases, but the central behavioral ambiguity is resolved.

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 concise sentences with no filler. The core result is front-loaded, and the side nuance is packed into the second sentence efficiently. Every clause 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?

For a 2-parameter query with full schema coverage, the description is mostly complete: it defines the result, the side semantics, and the schema covers currency format. It could benefit from a brief note on return format or when to prefer sibling tools like list_houses or get_rates, so it is not fully self-contained.

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 nuance to the 'side' parameter by explaining the price direction ('buy' = lowest sell price, 'sell' = highest buy price), which goes beyond the schema's generic phrasing. Currency is adequately documented in the schema as an ISO code.

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?

States the tool returns 'the single best casa de cambio right now to buy or sell a currency,' a specific outcome that distinguishes it from siblings like list_houses (all houses) and get_rates (rates). The side semantics are defined up front, leaving no ambiguity about what the tool does.

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 intended use is implied: call this when you need the current best exchange house for a given currency and side. However, it does not explicitly name alternatives or exclusions, such as using list_houses to see all exchange houses or get_rates for historical rates, so an agent must infer when this tool is preferred.

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 a key non-obvious behavior: foreign-to-foreign conversions route through UYU. It also reveals that rates come from the Uruguayan market, which is important context beyond 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?

Two sentences with no wasted words. The primary purpose is front-loaded, the important routing caveat is stated second, and the supported-codes list is compact.

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 conversion tool with no output schema, this description covers purpose, rate source, routing behavior, and supported codes. It does not describe the response shape, but the operation is straightforward enough that the missing output details are a minor gap.

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 amount, from, and to at 100% coverage, so the baseline is 3. The description adds value by listing supported currency codes and the routing-through-UYU behavior, which clarifies what from/to values mean in practice.

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?

States a specific verb and resource: convert an amount between currencies. It also adds a distinctive scope—best available Uruguayan market rates—and clarifies routing behavior, which makes it easy to tell apart from siblings like get_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 clearly implies this is the tool for currency conversion, but it does not explicitly say when to prefer it over related siblings such as get_rates. There is useful context about rate sourcing, but no direct alternative guidance or exclusion criteria.

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 of behavioral disclosure. It discloses that the output is AI-generated and changes meaning based on currency presence. However, it does not mention data freshness, response format, rate limits, or whether the analysis may be non-deterministic, which are relevant for an AI-generated tool.

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 remarkably concise: two sentences with no filler. The core purpose is front-loaded, the conditional currency behavior is clearly stated, and the language parameter is enumerated compactly. Every sentence contributes useful information.

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 low-complexity tool with two optional parameters and no output schema, the description provides enough context to invoke it correctly: what it does, how currency changes behavior, and which languages are supported. A more explicit statement of output type or freshness would be helpful, but the AI-generated analysis wording makes the expected output reasonably clear.

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 semantic value by explaining that omitting currency produces a whole-market summary, which the schema does not state. It also reiterates the lang enum values. This goes beyond a simple restatement of the 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's function: an AI-generated analysis of the Uruguayan exchange market, with a whole-market summary by default and per-currency analysis when a currency is supplied. It is specific about the domain and the conditional behavior. However, it does not explicitly distinguish itself from sibling tools such as get_rates or get_evolution.

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 conditional usage: use without currency for a whole-market summary, with currency for per-currency analysis. It also specifies supported language values. However, it gives no direct guidance on when to choose daily_summary over sibling tools like get_rates, get_news, or get_evolution, leaving the selection 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the output categories (series and statistics, including min/max/avg/current/change) and the time range, which is useful. However, it does not specify the output structure, data granularity (e.g., daily vs monthly points), or any other behavioral details like pagination or error conditions, leaving some uncertainty about what the caller receives.

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 sentences with zero waste. It front-loads the core purpose and follows with a single, relevant usage pointer. Every word earns its place, making it easy to parse quickly.

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 tool with three parameters and no output schema, the description explains the essential request semantics and points to a prerequisite tool. However, because there is no output schema, the description should more thoroughly describe the shape of the returned series or statistics. The current text is sufficient for making the call but not fully adequate for interpreting the result without additional assumptions.

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 value by mapping 'origin' to an exchange house and 'period' to months, which reinforces the schema. It also directs users to list_houses for valid origin IDs, going beyond the schema and genuinely helping the agent supply correct parameter values.

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: retrieving historical rate series and statistics (min/max/avg/current/change) for an exchange house and currency over a configurable number of months. It specifies the verb, resource, and key parameters, making its intent evident. However, it does not explicitly differentiate itself from sibling tools like get_rates or daily_summary, so it falls short of a perfect score.

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 for when to use the tool: when historical rate series and statistics are needed over the last N months. It also gives a concrete prerequisite instruction, 'Use list_houses for valid origin ids,' which aids correct invocation. It does not mention exclusions or alternatives such as 'for current rates, use get_rates instead,' but the context is nonetheless clear.

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. It discloses the data source (Google News), the deduplication behavior, and the sort order (newest first), which are the notable traits for a news fetcher. It omits minor operational details like caching or network behavior, but the core behaviors are visible.

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 sentence packs the source, scope, deduplication, and ordering without filler. The most important qualifier ('Latest') is front-loaded, and every clause 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?

For a one-parameter, read-only headline fetcher, this is nearly complete: scope, source, and result properties (deduplicated, sorted) are all present. The only minor gap is that no output schema exists and the description does not explicitly describe the headline item structure, though 'headlines' implies the expected content.

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 for the only parameter (limit) is 100%, including default and maximum, so the description does not need to add parameter details. The description adds context about result ordering but nothing about the limit parameter, which is acceptable at the baseline.

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-resource pair ('get' news) and narrows the scope to 'Uruguayan dollar/economy headlines' from Google News, making the purpose unmistakable. It also mentions deduplication and newest-first ordering, which clearly distinguishes it from sibling tools like get_rates or daily_summary.

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 current Uruguayan dollar/economy headlines, so an agent can infer when to use it. However, it does not explicitly state when not to use it or mention any sibling alternative, leaving routing 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?

With no annotations, the description carries the disclosure burden. It reveals the data source (Uruguayan exchange houses), the temporal scope (current), and an important exclusion (BCU/interbank), which is beyond what the schema provides. It does not mention freshness, rate limits, or response ordering, but for a simple read operation the key behavioral boundaries are present.

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 tight sentences that front-load the core purpose and then add output details, exclusions, and parameter format. Every sentence 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?

For a single-parameter read tool with no output schema, the description explains the main return contents sufficiently: averages, best house, spread, and full list. It does not specify exact response shape or error behavior, but those are minor for a simple rates lookup.

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 schema already documents the only parameter, currency, with an ISO-code description and examples. The description reinforces that currency is an ISO code like USD, EUR, ARS, or BRL, but adds no new semantics beyond the 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 states a specific verb and resource: retrieving current buy/sell rates for a given currency across Uruguayan exchange houses. It enumerates concrete outputs (market average, best buy/sell house, lowest spread, per-house list) and explicitly excludes BCU/interbank quotes, making it easy to distinguish 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 'Current buy/sell rates' provides clear context that this is the point-in-time snapshot tool, while the explicit exclusion of BCU and interbank quotes narrows when it applies. It does not name sibling alternatives such as get_evolution or best_house, so it stops short of explicit when/when-not 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?

There are no annotations, so the description carries the behavioral disclosure burden. It conveys the scope ('all ... tracked') and the output fields, which is useful, but it does not explicitly mention that the operation is read-only, whether it is paginated, or any other behavioral constraints. For a simple list operation this is adequate but not rich.

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 resource, scope, and return fields without any wasted words. Every part adds useful information.

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 parameterless listing tool with no output schema, the description is complete: it identifies the exact set of entities returned and the key attributes included. An agent can invoke it correctly without further clarification.

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 description does not need to explain parameter meaning. The baseline of 4 applies here; there is nothing missing in terms of parameter semantics.

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 operation ('list'), the resource ('all Uruguayan exchange houses / casas de cambio'), and the return fields (display name, website, departments served). This distinguishes it from siblings like get_rates or best_house, which deal with rates and rankings rather than the full registry.

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 best_house or get_rates. It implies a generic listing use case but does not explicitly state when it is the right choice or when a sibling should be preferred.

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