SigmaDiario — Datos de Argentina

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 adds useful context: the quotes are 'now' (current) and include a 30-day historical series, plus compra/venta buy/sell values. However, it does not disclose data source, update frequency, response structure, or any limits. This is acceptable for a simple read tool, but not richly transparent.

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, dense sentence that front-loads the core purpose ('Cotizaciones del dólar en Argentina ahora') and then packs all relevant variants into a parenthetical list. Every clause earns its place, 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 zero-parameter, read-only quote tool with no output schema and no annotations, this description covers the essential semantics: rate types, buy/sell, and 30-day series. It does not describe the exact JSON shape or update frequency, but those are secondary given the tool's simplicity. Slightly more detail on the response format would make it 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 the description has no obligation to explain inputs. The baseline of 4 applies. The description instead tells the agent what data variants to expect, which is more valuable than parameter documentation for a parameteless endpoint.

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 a specific resource — Argentine dollar exchange rates — and enumerates the exact quote types covered (blue, oficial, MEP, CCL, tarjeta, cripto) plus buy/sell and 30-day series. This fully distinguishes it from sibling tools like get_feriados or get_inflacion, which deal with unrelated domains.

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 context is unmistakable: use this tool when you need current Argentine dollar quotes across specific rate types. It does not explicitly state when not to use it, but the sibling tools are in clearly different domains, so no exclusion is strictly necessary. Lacks an explicit pointer to alternatives, but not needed here.

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 present, so the description carries the burden. It discloses the core behavior — listing upcoming holidays with countdown days — but does not mention output format, ordering, or edge cases such as whether 'próximos' includes today or starts tomorrow.

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 with no filler. The core function is front-loaded and every word adds value.

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, read-only tool with no output schema, the description covers the essential return value: upcoming holidays and days remaining. It doesn't specify the exact fields or time horizon, but it is sufficient for this level of simplicity.

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, and the base expectation for a 0-parameter tool is a 4. The description adds no ambiguity about inputs and correctly implies the tool needs 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?

The description states the resource (Argentina's holidays) and the specific data delivered (upcoming holiday dates with days remaining). It is distinct from siblings like get_dolar or get_inflacion, though it lacks an explicit verb such as 'get' or 'return'.

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/when-not guidance or alternative tool references, but the domain-specific name and description make it clear this is the tool for Argentine holidays. Usage is implied rather than stated.

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 present, so the description carries the full behavioral burden. It discloses the data type (monthly/interannual CPI) and source (INDEC) and implies a read-only historical series, but does not mention response format, pagination, rate limits, or any side effects. For a simple retrieval tool, this is partial but not comprehensive.

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 immediately identifies the subject ('Inflación de Argentina') and packs the key attributes (IPC mensual e interanual, INDEC, serie histórica) with 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, read-only data retrieval tool, the description provides the essential details: topic, source, and temporal scope. It omits output structure, but without an output schema or expected usage complexity, the description is adequate for an agent to make a correct call.

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 has zero parameters, so the baseline is 4. The description adds no parameter semantics (there are none to explain), and there is no ambiguity since the schema is empty and 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 identifies the resource (Argentine inflation data) and the specific content (monthly and year-over-year CPI from INDEC with historical series). It distinguishes from sibling tools like get_dolar and get_feriados by the topic, so an agent can unambiguously select it for inflation queries.

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 explicit guidance on when to use this tool versus alternatives. While the sibling tools cover different domains (currency, holidays, etc.), the description does not state context, prerequisites, or exclusions, leaving the agent to infer usage from the tool name and topic.

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 full burden and does state the output contains latest results and jackpots, implying a read-only query. However, it omits details such as update frequency, data source, or response structure, leaving some behavioral ambiguity.

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 with no redundant words. It states the core purpose and output in one line, which is ideal for a simple parameterless 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?

For a tool with no parameters and no output schema, the description covers the essential output (results and jackpots) and scope (latest draws of four specific games). It does not describe the response format, which an agent might need for parsing, but this is a minor gap given the tool's simplicity.

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 and the schema is empty, so there is nothing for the description to explain. The phrasing 'latest results' implicitly confirms no filtering options exist, matching the baseline of 4 for parameterless tools.

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 specifies the resource (latest results) and names the exact games covered (Quini 6, Loto Plus, Brinco, Telekino), making the tool's function immediately clear. This also distinguishes it from siblings like get_quiniela, which covers a different lottery game.

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 offers no guidance on when to use this tool instead of siblings, particularly get_quiniela which is likely to be confused with it. There are no conditions, exclusions, or alternative routing hints.

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 must carry the transparency burden. It communicates that the tool returns today's quiniela heads per lottery and per draw, but it does not clarify operational details such as when results become available, timezone assumptions, or whether only heads are included rather than full prize tables.

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 focused sentence with no filler. It front-loads the main resource and then adds the relevant detail about lotteries and per-draw granularity.

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 read-only data tool, the description covers the key facts: scope (today), content (heads), breakdown (by lottery, per draw). The main gap is the absence of any expected response shape, though no output schema exists and the complexity is low.

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 takes zero parameters and the schema already states this, so the baseline of 4 applies. There is no parameter information for the description to add.

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 resource: today's Argentine quiniela results, broken out by lottery and draw. It does not, however, explicitly contrast itself with the sibling get_loterias, so it stops short of full differentiation.

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 for today's quiniela results rather than general lottery information. No when-not-to-use conditions or references to alternative tools are given.

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 reveals the data provenance (official Prefectura Naval data), the exact rivers covered, the port-by-port granularity, and the temporal scope ('hoy'). It does not describe response structure or failure modes, but for a zero-parameter read the key behavior is communicated.

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 with no filler. The main subject, river heights, is front-loaded, followed by the key qualifiers: which rivers, temporal scope, source, and daily variation.

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 parameterless retrieval, the description is largely complete: it names the source, the rivers, the granularity, and the temporal scope. It could additionally state exact units or response format, but those are minor omissions for such a low-complexity tool.

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 and the schema fully covers that, so the baseline 4 applies. The description adds useful context about what data the no-argument call returns, even though there are no parameters to explain.

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 a specific resource: today's river heights for the Paraná, Paraguay, and Uruguay rivers, port by port, with daily variation. It also cites the official source, Prefectura Naval, which makes the purpose unmistakable and clearly distinct from the unrelated 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 does not explicitly state when to use this tool versus alternatives, but the domain is specific enough that an agent can infer it is for river-height queries. None of the sibling tools appear to compete for the same domain, so the lack of explicit exclusion guidance is a minor gap.

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 explains the core function but omits useful behavioral details such as ordering, whether the keyword match is case-sensitive, pagination behavior, or rate limits. This is a meaningful gap for a tool with no annotation safety hints.

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. It communicates the essential purpose and optional filter behavior efficiently, which is ideal for an agent scanning tool definitions.

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, the description and schema together cover the parameters and the general output shape (headlines). It lacks explicit notes about output format, ordering, or edge cases, but nothing critical is missing for an agent to invoke 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?

The input schema already provides 100% description coverage for both parameters, including the limit range and default. The description only adds that results can be filtered by keyword, which is minimal additional value 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, resource, and scope: retrieving recent headlines from SigmaDiario, optionally filtered by keyword. It is clearly distinguishable from the sibling tools, which retrieve entirely different data categories like dollar rates, holidays, or lottery results.

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 use case is implied by the name and description: use this when news headlines from SigmaDiario are needed. However, there is no explicit guidance about when not to use it or how it relates to sibling tools, so the agent must infer the appropriate context.

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