disease

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 describes the return data (cases, deaths, etc.) but lacks critical behavioral details such as data freshness (e.g., real-time vs. delayed), error handling (e.g., for invalid country names), rate limits, or authentication requirements. This is a significant gap for a tool with no annotation coverage.

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, efficient sentence that front-loads the core purpose ('Get COVID-19 statistics for a specific country') and follows with essential return details. Every word earns its place, with no redundant or vague phrasing, making it highly concise and well-structured.

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 moderate complexity (single parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and return data, but lacks behavioral context (e.g., data sources, update frequency) and does not compensate for the absence of an output schema by detailing response structure. This meets minimum viability with clear 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 description coverage is 100%, with the parameter 'country' fully documented in the schema (type, required, description with examples). The description does not add any parameter-specific information beyond what the schema provides, such as format constraints or validation rules, so it meets the baseline for high schema coverage.

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 specific action ('Get COVID-19 statistics') and resource ('for a specific country'), distinguishing it from sibling tools like get_global_stats (global data), get_historical (time-series), and get_vaccine_stats (vaccination data). It explicitly lists the returned metrics (cases, deaths, recovered, etc.), making the purpose unambiguous.

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 implicitly suggests usage for country-specific COVID-19 data, but does not explicitly state when to use this tool versus alternatives like get_global_stats or get_vaccine_stats. It provides clear context (COVID-19 statistics for a country) without exclusions or prerequisites, falling short of naming specific alternatives.

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 clearly describes the return data (total cases, deaths, etc.), which is helpful, but lacks details on data freshness, sources, error handling, or rate limits. It adequately covers the core behavior but misses operational context.

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, well-structured sentence that front-loads the purpose ('Get global COVID-19 statistics') and efficiently lists the returned data. Every word adds value, with no redundancy or wasted space, making it highly concise 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?

Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is nearly complete: it states the purpose, usage context, and return data. However, it could improve by mentioning data sources or update frequency, slightly reducing completeness for a statistical 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 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately omits parameter details, focusing on the tool's function. A baseline of 4 is applied for zero-parameter tools, as it efficiently avoids unnecessary 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 specific action ('Get global COVID-19 statistics') and resource ('global COVID-19 statistics'), distinguishing it from sibling tools like get_country_stats (country-specific) and get_historical (time-series data). It provides concrete details about what data is returned, making the purpose unambiguous.

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 implicitly suggests usage for global-level COVID-19 data, with sibling tools indicating alternatives for country-specific, historical, or vaccine-related statistics. However, it lacks explicit guidance on when to choose this tool over others or any prerequisites, keeping it from a perfect score.

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 describes what data is returned (daily timeline of cases, deaths, recoveries) and the scope (country or global), which is useful. However, it doesn't mention important behavioral aspects like rate limits, data freshness, error conditions, or whether this is a read-only operation (though 'get' implies read). The description adds some value but lacks comprehensive behavioral context.

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 and well-structured in just two sentences. The first sentence clearly states the purpose and scope, while the second sentence specifies the return format. Every word earns its place with zero waste or redundancy, making it easy for an agent to quickly understand the tool's function.

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 moderate complexity (historical data retrieval with optional parameters), no annotations, and no output schema, the description provides adequate but incomplete context. It covers the basic purpose and return data types but lacks information about response format structure, data sources, limitations, or error handling. For a data retrieval tool with no output schema, more detail about the return structure would be 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?

The input schema has 100% description coverage, with both parameters (days and country) well-documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.

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: 'Get historical COVID-19 timeline data for a country or globally' with specific resources (cases, deaths, recoveries) and a daily timeline format. It distinguishes from sibling tools like get_vaccine_stats by focusing on historical case data rather than vaccination statistics. However, it doesn't explicitly differentiate from get_country_stats or get_global_stats which might overlap in scope.

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 context by specifying 'for a country or globally' and mentioning the return format, but it doesn't provide explicit guidance on when to use this tool versus alternatives like get_country_stats or get_global_stats. No when-not-to-use scenarios or prerequisites are mentioned, leaving the agent to infer appropriate usage from the tool name and description alone.

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 describes the return format ('daily cumulative vaccine doses administered') and time scope ('over the last 30 days'), which adds useful context. However, it does not cover aspects like rate limits, error handling, or data freshness, leaving gaps in behavioral understanding for a tool that likely queries external data.

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 highly concise and front-loaded, consisting of two sentences that efficiently convey the tool's purpose and return data. Every sentence earns its place by adding value: the first states the action and resource, and the second specifies the data format and time range, 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 moderate complexity (querying vaccination data with an optional parameter) and no annotations or output schema, the description is adequate but incomplete. It covers the core functionality and return scope, but lacks details on output structure (e.g., data format, fields), error cases, or prerequisites, which would be helpful for an AI 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% description coverage, with the 'country' parameter well-documented in the schema ('Country name to get vaccine data for. Omit for global totals.'). The description does not add any parameter-specific information beyond what the schema provides, such as format examples or constraints, so it meets the baseline score of 3 where the schema does the heavy lifting.

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: 'Get COVID-19 vaccination coverage timeline' specifies the verb ('Get') and resource ('vaccination coverage timeline'), and 'Returns daily cumulative vaccine doses administered over the last 30 days' elaborates on the data returned. It distinguishes from siblings like 'get_country_stats' by focusing specifically on vaccination data rather than general statistics.

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 by specifying what data is returned (vaccination coverage over the last 30 days), but does not explicitly state when to use this tool versus alternatives like 'get_global_stats' or 'get_historical'. It provides some context (e.g., 'Omit for global totals' in the schema hints at a default), but lacks clear guidance on exclusions or direct comparisons to sibling tools.

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