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 key behavioral traits: 'Sem chamadas de rede; resposta instantânea' (no network calls, instant response). However, it does not describe behavior for invalid inputs, missing data, or how the output is structured beyond what is implied by the schema. This is adequate 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 two sentences, front-loaded with the main purpose. Every sentence is informative: the first defines the action and source, the second clarifies data nature (static, instant). No unnecessary words. Highly concise.

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 tool is simple with only two parameters, both well-described in the schema. The description covers core functionality and data source. However, it lacks usage guidelines and deeper behavioral context (e.g., error handling, output format). Given the schema and output schema exist, the description is minimally complete but could be more helpful.

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%, and the schema already provides rich parameter details (e.g., accepted crop names, aliases, region formats). The description adds only 'Dado estático curado a partir do CONAB' which reinforces data source but does not significantly enhance understanding beyond the schema. Baseline 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 tool's purpose: 'Consulta o calendário de plantio e colheita de uma cultura por região (CONAB).' It specifies the verb (consulta), resource (calendário), and scope (by crop and region). Compared to sibling tools like cotacao_soja or noticias_agro, it is well differentiated as a calendar lookup tool.

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. It mentions that data is static and instant, but does not specify context where other tools (e.g., cotacao_* for pricing) would be more appropriate. No when-not-to-use or prerequisite conditions 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?

With no annotations, the description carries full burden. It discloses the data source (BCB Olinda/PTAX), explains PTAX as official reference rate, and specifies behavior on weekends/holidays (returns last business day). This is good behavioral context, though rate limits or error handling are not mentioned.

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 paragraphs. The first sentence states the primary purpose, followed by source and key behavioral note. The second paragraph lists return fields. No unnecessary words, and critical 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 parameterless tool with an output schema (present but not shown), the description fully explains what the tool does, its data source, behavior on non-business days, and the structure of the return value. No additional information seems necessary for correct usage.

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 schema coverage is 100%. The description adds value by detailing the return fields (fonte, moeda_origem, compra, venda, etc.), which compensates for the absence of parameter documentation. Baseline for no parameters is 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 returns the official PTAX quotation for USD/BRL from Brazil's Central Bank. The verb 'retorna' (returns) and specific resource 'cotação PTAX oficial do dólar americano' leave no ambiguity. Sibling tools are all agricultural, so this currency tool is 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?

No explicit when-to-use or when-not-to-use guidance is provided. The description implies usage for obtaining official exchange rates, but does not mention alternatives or scenarios where other tools might be preferred. Given no similar tools among siblings, the lack is less critical but still a 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 burden. It discloses the data source (Open-Meteo), that no token is needed, and that it performs geocoding. This gives the agent a good sense of behavior. It does not mention read-only or rate limits, but the read-only nature is implied by 'previsão' and the open API.

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?

Three short sentences, each providing essential information: function, source, and a key behavior (geocoding). There is no redundancy or unnecessary 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?

With an output schema present (not shown but indicated), the description does not need to explain return values. It covers the input parameters well, notes the default for 'dias', and explains the geocoding step. It is complete enough for this relatively simple 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?

Schema coverage is 100%, so the schema already describes both parameters. The description adds value by explaining that the city name is resolved via geocoding and provides examples. This contextual information helps the agent understand how parameters are used 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 clearly states the tool returns weather forecasts for Brazilian cities. It is distinct from sibling tools which are all agricultural (cotacao, noticias, etc.), so there is no ambiguity about purpose.

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 context (city name, data source) but does not explicitly state when to use over siblings. However, the purpose is so distinct that usage is implicitly clear. No when-not-to-use 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 are provided, so the description fully carries the burden. It accurately describes the tool as a converter of weight or area units with no side effects. Could further mention statelessness or read-only nature, but current text is sufficiently 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?

The description is a single, front-loaded sentence with no unnecessary words. It efficiently covers the tool's core function 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?

Given the tool's simplicity and presence of an output schema, the description adequately covers purpose and required parameters. No missing critical information for a conversion 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?

Schema coverage is 100%, and the description adds minimal extra context ('agronegócio (peso ou área)'). The case-insensitivity hint is already in the schema. Baseline 3 is appropriate as the description does not significantly enhance parameter understanding 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 clearly states the function: converting values between agribusiness units (weight or area). It uses a specific verb ('converte') and resource ('valor'), distinguishing it from sibling tools which focus on market data and weather.

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 specify when to use this tool over alternatives. However, the context of sibling tools (all non-conversion) implies its unique role. No guidance on prerequisites or when not to use.

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 covers the fallback mechanism and data sourcing. It discloses that it uses Scot Consultoria as primary and ESALQ/B3 as fallback, adding transparency beyond a simple read operation.

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, no filler. Front-loaded with purpose, followed by source details. Every sentence contributes essential 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?

Given the simple tool with one parameter and an existing output schema, the description is fully adequate. It covers data sources and fallback, leaving no critical gaps.

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 has 100% coverage for the single parameter. The description adds value by explaining the fallback behavior if the praça is not found, which the schema does not address.

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 clearly states it returns the quotation of boi gordo for a Brazilian regional market. It distinguishes from sibling tools like cotacao_leite and cotacao_milho by specifying the commodity. The mention of primary and fallback sources adds clarity.

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?

Description implies when to use (for boi gordo quotes) but does not explicitly state when not to use or name alternatives. The fallback behavior provides context on handling missing parameters, which is helpful.

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 must fully disclose behavioral traits. It mentions the data source (CEPEA) and states, but lacks details on access restrictions, data freshness, update frequency, or whether the operation is read-only. As a price lookup, it is presumably safe, but this is not explicitly stated.

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: two sentences and a list. It is front-loaded with the main purpose and then provides supporting information. Every sentence is relevant and 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?

Given the low complexity (single parameter) and the presence of an output schema, the description adequately covers source and available states. It could mention the return format or data type, but that is handled by the output schema. The description is complete enough for a simple lookup 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?

Schema coverage is 100% for the single parameter 'estado', with a good description including default and examples. The tool description adds value by listing all valid states explicitly, which goes beyond the schema's individual examples. This helps the agent fill 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?

The description explicitly states 'Retorna o preço ao produtor de leite (CEPEA) por estado.' This is a specific verb ('retorna') and resource ('preço ao produtor de leite') with a clear scope ('por estado'). It readily distinguishes from sibling tools like 'cotacao_boi_gordo' or 'cotacao_milho' which deal with different commodities.

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 a list of available states and the data source, but does not offer explicit guidance on when to use this tool versus alternatives, nor does it mention any conditions or prerequisites. The usage is implied by the commodity name, but no exclusions or comparative advice 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?

With no annotations provided, the description carries the full burden. It describes the return format and mentions caching ('cache_hit'), indicating it is a read-only operation. However, it does not disclose update frequency, authentication needs, or potential rate limits.

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 with two lines of prose and a bullet list of return fields. It is front-loaded with purpose and lacks unnecessary fluff, though the bullet list could be integrated more naturally.

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 zero parameters and a described output schema, the description covers the basic return structure and source. However, it lacks temporal context (e.g., latest vs. historical prices) and any caveats about data freshness, which would improve completeness.

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 has no parameters, so the baseline is 4. The description does not need to add parameter information, but it does describe the return fields, which is helpful 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 it returns the CEPEA/ESALQ indicator for corn, a national price reference. The verb 'Retorna' and specific resource 'indicador CEPEA/ESALQ de milho' provide clear purpose, distinguishing it from sibling tools like cotacao_soja by product.

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 lacks any guidance on when to use this tool versus alternatives. It does not mention prerequisites, when not to use, or how it compares to other cotacao_* 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?

Without annotations, the description carries the full burden but only describes the return format and source. It does not disclose behavioral traits such as idempotency, rate limits, or authentication requirements, though the output includes a cache_hit field hinting at caching behavior.

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, front-loading the core purpose, then listing the return fields in a clear bullet format. Every sentence serves a purpose.

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 tool is simple with no parameters, and the output schema details the return fields. The description covers source and reference, but lacks information on update frequency or data freshness, which would be relevant for a price indicator.

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, so the schema provides full coverage. The description adds value by detailing the return fields, which is helpful for understanding the output.

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 the CEPEA/ESALQ soybean indicator for the Port of Paranaguá, a specific and well-known benchmark. It distinguishes itself from sibling tools like cotacao_boi_gordo and cotacao_milho by specifying the commodity.

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 like indicador_esalq or other cotacao tools. While the commodity specificity implies usage, no when-not or exclusionary guidance is provided.

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 exist, so the description must carry the burden. It discloses the source (Comex Stat / MDIC), API type (public), and data latency (1-2 months). This is sufficient for a read-only operation, though it could mention that no authentication is needed.

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 only two sentences: the first defines purpose and output, the second provides source context. Every sentence adds value, and it is front-loaded with the main action.

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 tool has one parameter, no required fields, and an output schema (not shown), the description is complete. It explains what the tool does, its source, and data latency, which is sufficient for an agent to use 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 has 100% coverage for the single parameter 'produto', including its allowed values. The description does not add any additional meaning beyond the schema, so it meets the baseline of 3.

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 'Retorna dados de exportação do agronegócio brasileiro (volume e valor FOB)', specifying the exact data (export volumes and FOB value). It is distinct from sibling tools like cotacao_soja (prices) and calendario_safra (harvest calendar).

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 context (source, data lag) but does not explicitly state when to use this tool versus alternatives. It implies usage for export data, but lacks exclusion criteria for sibling 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 provided, but the description fully discloses that the tool returns honest information about the situation and alternatives rather than actual quotes due to licensing issues. It clearly sets expectations and warns about dependencies.

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?

Description is front-loaded with key purpose and warnings. It's slightly verbose (repeating the caveat about honest info) but every sentence adds value for a tool with data restrictions.

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 tool's limited data availability and reliance on licensed sources, the description covers all necessary context: what it does, data source limitations, alternatives, and single parameter. Output schema exists, so return values need not be explained.

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% and provides complete parameter info (contract codes, defaults). The tool description does not add extra semantics beyond what's already in the schema, so baseline 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 tool 'retorna informações sobre futuros agrícolas da B3' and lists specific commodities (boi gordo, soja, milho, café). It also distinguishes from sibling tools by recommending spot price alternatives.

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?

Explicitly says when to use the tool (for B3 futures) and when not ('Para preços spot use...') with specific sibling tool names. Also warns about data source licensing and potential HTTP 401 errors.

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 must carry the burden. It mentions the output structure and source but fails to disclose behavioral traits like data freshness, rate limits, or any potential side effects. Minimal 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 with approximately 5 lines, front-loading the purpose. It efficiently conveys the key information without unnecessary 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?

Given no parameters, the description covers the return structure adequately. However, it omits details like update frequency or data freshness. With a described output schema (though not formal), completeness is moderate.

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 (baseline 4). The description adds value by detailing the output dictionary fields, which compensates for the lack of parameter documentation.

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 the national ESALQ/B3 indicator for fat cattle, with a specific verb 'Retorna'. It distinguishes from siblings like 'cotacao_boi_gordo' by emphasizing its role as a national benchmark.

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 alternatives such as 'cotacao_boi_gordo'. The description only states what it does, not when it should be chosen.

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 provided, but the description clearly states the return format (dict with pracas, total, provider, nota). No side effects or permissions mentioned, but adequate for a read-only list operation.

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 sentence plus a bullet list of return fields; no unnecessary words. Front-loaded and concise.

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 no parameters and an output schema (albeit not shown), the description fully explains the return structure. Complete for a simple list 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?

No parameters exist, so the description adds no param info. Schema coverage is 100% trivially, so baseline 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 states the tool lists available 'praças' for 'boi gordo' quotations via Scot Consultoria. It distinguishes from siblings like 'cotacao_boi_gordo' which provides the actual quotation.

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 implied as a prerequisite for obtaining quotations for specific praças, but no explicit when-to-use or alternatives 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?

No annotations provided, so the description bears full burden. It explains the return format (dictionary with keys) but lacks details on side effects, permissions, or data freshness. Adequate for a simple read operation.

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, front-loaded with purpose, returns clearly stated. 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?

Given the tool's simplicity (no parameters) and presence of an output schema, the description provides sufficient context. Could be slightly improved by clarifying scope (e.g., 'all products with available quotes').

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?

With zero parameters and 100% schema coverage, the description adds no extra meaning, which is acceptable. Baseline for 0 params is 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 verb 'listar' and resource 'produtos' clearly state the tool's action. It distinguishes from siblings like 'cotacao_boi_gordo' by focusing on all products with quotes rather than a specific commodity.

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 usage for getting a list of all products with quotes, but does not explicitly mention when to use this tool versus specific cotação tools or alternatives like 'listar_pracas'.

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?

Although no annotations are present, the description implies a safe, read-only operation by describing a list retrieval. It adds value by detailing the return structure, which goes beyond the empty input schema. However, it does not explicitly state that the operation is non-destructive, but for a 0-parameter list tool, this is sufficient.

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 for purpose and a bullet-point structure for the return value. It is front-loaded and every part is essential, 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?

Given the tool's simplicity (no parameters, no annotations, and a clear return structure described), the description is complete. It tells the agent exactly what the tool does and what to expect as output, which is all that is needed for selection and invocation.

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?

With zero parameters and 100% schema coverage, the baseline is 4. The description does not need to add parameter details as there are none, and it correctly omits any irrelevant information.

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 all supported units for a unit converter, specifying the return structure with weight and area lists. It distinguishes itself from sibling tools like 'converter' which likely performs the actual conversion.

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 guidance is provided on when to use this tool versus alternatives. It is implied that it should be used before converting units, but this is not explicitly stated, leaving the agent without clear usage context.

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 full burden. It discloses the data source (Canal Rural) and method (RSS), indicating a read-only, non-destructive operation. While not exhaustive, it is sufficiently transparent for a simple news retrieval 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 consists of two concise sentences: the first states the core function, and the second provides the source. Every sentence is necessary, and the structure is front-loaded 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 tool is simple with low complexity. The presence of an output schema means return values are documented elsewhere. The description covers the main purpose and source, and the schema parameters are well-documented. It is complete enough for this 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?

Schema description coverage is 100% with clear descriptions for tema and limite. The tool description does not add any additional meaning beyond the schema, so baseline score 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 it returns latest agribusiness news via RSS, specifying the source (Canal Rural). The verb 'Retorna' and resource 'notícias do agronegócio' are specific, and it naturally distinguishes from sibling tools that focus on prices or calendars.

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 this tool is for retrieving agribusiness news, but it does not explicitly state when to use it over alternatives like cotacao_soja or clima_previsao. There is no comparison or when-not guidance, only implied context.

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