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

With no annotations provided, the description carries the full burden. It discloses the tool's behavior by specifying the geographic coverage and return fields, but lacks details on rate limits, authentication needs, error handling, or whether it's read-only (implied by 'Get' but not explicit). It adds some context but leaves gaps in behavioral traits.

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 front-loaded with the core purpose in the first sentence, followed by additional details in a second sentence. Every sentence earns its place by adding geographic scope and output specifics without redundancy, making it appropriately sized and efficient.

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 (1 parameter, no output schema, no annotations), the description is fairly complete. It covers purpose, scope, and return fields, but lacks output format details (e.g., structure of returned data) and error scenarios. Since there's no output schema, more detail on returns would be beneficial, but it's adequate for basic use.

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 parameter 'q' documented as 'Location query — city name, lat/lon, zip, postcode, IATA, or IP.' The description does not add meaning beyond this, as it only mentions 'for a location' without elaborating on parameter usage. Baseline 3 is appropriate since 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 specific verb ('Get') and resource ('active government weather alerts and warnings for a location'), with explicit geographic scope ('USA, UK, Europe, and rest of world') and output details ('headline, severity, urgency, affected areas, and full description'). It distinguishes from siblings like get_current_weather or get_forecast by focusing on alerts/warnings rather than general weather data.

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 this tool ('for a location' and geographic coverage), implying it's for alert retrieval rather than other weather data. However, it does not explicitly state when not to use it or name alternatives among siblings (e.g., no comparison to get_current_weather for non-alert weather info), so it falls short of 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 states what data is returned but doesn't mention critical behavioral aspects like whether this is a read-only operation, if it requires authentication, rate limits, error conditions, or response format. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.

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 and lists all returned data points without any wasted words. Every element serves a clear purpose, 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 (2 required parameters, no output schema, no annotations), the description is partially complete. It clearly states what data is returned but lacks behavioral context and output details. Without annotations or an output schema, the description should do more to compensate, but it at least defines the core purpose adequately.

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 description coverage is 100%, with both parameters (q and dt) fully documented in the schema. The description adds minimal value beyond the schema by mentioning 'location and date' but doesn't provide additional syntax, format details, or constraints. This meets the baseline of 3 when 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 with a specific verb ('Get') and resource ('astronomy data'), and lists the specific data points returned (sunrise, sunset, moonrise, moonset, moon phase, moon illumination percentage). It distinguishes itself from siblings like get_current_weather or get_forecast by focusing exclusively on astronomical events.

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 guidance on when to use this tool versus alternatives. While it implicitly suggests use for astronomical data, it doesn't mention when not to use it (e.g., for weather data) or refer to sibling tools like get_timezone for time-related queries or get_history for past data. Usage context is implied but not explicit.

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 data (temperature, wind, etc.) and optional air quality, but lacks details on rate limits, authentication needs, error handling, or data freshness. 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 front-loaded with the core purpose in the first sentence, followed by details on returns and optional features. Both sentences earn their place by adding value, with zero wasted words, making it highly efficient.

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 annotations and no output schema, the description partially compensates by listing return data fields. However, it lacks details on response structure, error cases, or operational constraints, leaving gaps for a tool with two parameters and no structured safety or output information.

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 schema fully documents both parameters. The description adds minimal value beyond the schema by mentioning 'any location' and 'optionally includes air quality (AQI) data,' but does not provide additional syntax or format details. Baseline 3 is appropriate when 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 with specific verbs ('Get real-time current weather') and resources ('for any location'), distinguishing it from siblings like get_forecast or get_history by focusing on current conditions rather than predictions or past data.

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 current weather data but does not explicitly state when to use this tool versus alternatives like get_forecast or get_history. It mentions optional air quality inclusion, which provides some context but lacks explicit guidance on tool selection.

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 effectively describes the tool's behavior by specifying the forecast range (1-14 days), return data types (daily summaries, hourly breakdowns, current conditions, etc.), and optional inclusions (alerts, air quality). However, it lacks details on rate limits, authentication needs, error handling, or data freshness.

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 efficiently structured in two sentences with zero waste. The first sentence states the core purpose and key details (forecast range, return data). The second adds supplementary information (optional alerts and air quality). Every element serves a clear 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?

Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is adequate but has gaps. It covers the core functionality and data returns well, but lacks usage guidance relative to siblings and doesn't address behavioral aspects like rate limits or error handling. Without an output schema, more detail on 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, so the schema already documents all parameters thoroughly. The description adds marginal value by implying the 'days' parameter range (1-14) and mentioning optional alerts and air quality, but doesn't provide additional syntax or format details beyond what the schema specifies. Baseline 3 is appropriate when 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 with specific verbs ('Get weather forecast') and resources ('daily summaries', 'hourly breakdowns', 'current conditions', etc.). It distinguishes from siblings by specifying the comprehensive forecast scope (1-14 days with multiple data types), unlike more specialized tools like get_current_weather or get_astronomy.

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 guidance on when to use this tool versus its siblings. While it implies a comprehensive forecast, it doesn't explicitly state when to choose this over get_current_weather for current data only, get_future_weather for future forecasts, or get_astronomy for astronomy data alone. No exclusions or prerequisites are mentioned.

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 adds useful context about the return format ('Returns 3-hourly data') and access restrictions ('Pro+ plan and above'), but doesn't cover other behavioral aspects like error conditions, rate limits, authentication needs, or what happens with invalid dates beyond the range.

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 (three short sentences) with zero wasted words. Each sentence adds distinct value: purpose, return format, and access restrictions. It's perfectly front-loaded with the core functionality.

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 (future weather forecasting), no annotations, and no output schema, the description does well by covering purpose, timeframe, return granularity, and access tier. However, it lacks details about response format, error handling, and how it differs from similar siblings like get_forecast, leaving some gaps for an agent.

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 schema already fully documents both parameters. The description reinforces the date range constraint for 'dt' but doesn't add meaningful semantic context beyond what the schema provides. Baseline 3 is appropriate when 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 specific action ('Get future weather forecast'), resource ('weather'), and scope ('for a date between 14 and 300 days from today'). It distinguishes from siblings like get_current_weather (present) and get_forecast (likely short-term) by specifying the long-range future timeframe.

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 on when to use this tool (for dates 14-300 days ahead) and mentions availability constraints ('Pro+ plan and above'), which helps differentiate from other weather tools. However, it doesn't explicitly state when not to use it or name specific alternatives among siblings.

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 full burden. It discloses the temporal scope ('from 1 January 2010 onwards') and return format ('daily summary and full hourly breakdown'), which are useful behavioral traits. However, it doesn't mention rate limits, authentication requirements, error conditions, or pagination behavior that would be valuable for a data 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 is efficiently structured in two sentences. The first sentence states the core functionality and constraints, while the second provides usage context. Every phrase earns its place with no redundant 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 data retrieval tool with no annotations and no output schema, the description provides good context about what data is returned and temporal constraints. However, it could be more complete by mentioning authentication requirements, rate limits, or error handling. The 100% schema coverage helps compensate for some 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%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema - it mentions the date constraint but doesn't provide additional semantic context about parameter usage or interactions. Baseline 3 is appropriate when 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 with specific verbs ('Get historical weather data') and resources ('daily summary and full hourly breakdown'). It distinguishes from siblings by specifying historical data from 2010 onward, unlike get_current_weather or get_forecast which focus on current/future data.

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 this tool ('past weather lookups, analytics, and backtesting'), which implicitly distinguishes it from siblings focused on current, future, or specialized weather data. However, it doesn't explicitly state when NOT to use it or name specific alternatives among siblings.

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 mentions that tide data requires 'Pro+ plan and above,' which adds useful context about access restrictions. However, it does not disclose other behavioral traits such as rate limits, authentication needs, response format, or whether this is a read-only operation, leaving significant gaps 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 concise and front-loaded, stating the purpose in the first sentence and adding context in the second. Both sentences earn their place by specifying forecast elements and usage scenario, with no wasted words, though it could be slightly more structured for clarity.

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 annotations and no output schema, the description provides basic purpose and some context but is incomplete. It covers what the tool does and a usage hint, but lacks details on behavior, response format, and full parameter guidance. For a tool with 3 parameters and no structured support, this is adequate but has 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?

The input schema has 100% description coverage, so the schema already documents all parameters well. The description adds minimal value by mentioning tide data as optional, but does not provide additional semantics beyond what the schema specifies (e.g., it doesn't explain the significance of 'days' or 'q' beyond the schema's descriptions). Baseline 3 is appropriate given 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 tool's function: 'Get marine and sailing weather forecast' with specific elements like wave height, swell, and tide data. It distinguishes from general weather tools by focusing on marine aspects, though it doesn't explicitly differentiate from all siblings like 'get_current_weather' or 'get_forecast' which might also provide some overlapping data.

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 with 'Useful for nautical and coastal planning,' which suggests when to use it. However, it lacks explicit guidance on when to choose this tool over alternatives like 'get_forecast' or 'get_current_weather,' and does not mention any exclusions or prerequisites beyond the optional tide data note.

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 implies a read-only operation but doesn't disclose behavioral traits like rate limits, authentication needs, error handling, or data freshness. The description adds minimal context beyond the basic function, leaving gaps in understanding how the tool behaves in practice.

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 function and details. It wastes no words, clearly stating what the tool does, the sports covered, and the returned data fields. Every element 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 tool's moderate complexity (location-based sports event lookup), no annotations, and no output schema, the description is minimally adequate. It covers the purpose and data fields but lacks behavioral details, usage context, and output format. It meets the baseline for a simple query tool but doesn't fully compensate for missing structured information.

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 single parameter 'q' documented as a location query. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 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: retrieving upcoming sports events for specific sports (football/soccer, cricket, golf) with location-based filtering. It specifies the data fields returned (stadium, country, tournament name, start time). However, it doesn't explicitly differentiate from sibling tools like 'get_alerts' or 'get_current_weather' which serve different 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 description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, limitations, or compare it to sibling tools that might overlap in functionality (like 'search_locations' for location data). The user must infer usage from the purpose 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. It discloses the return format (IANA timezone ID, local time string, unix epoch) and that it queries locations, but lacks details on error handling, rate limits, authentication needs, or data sources. It adds some behavioral context but not comprehensively.

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 front-loaded and concise with a single sentence that efficiently conveys purpose and return values, with no wasted words. Every part earns its place by specifying what is retrieved and the output format.

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 low complexity (1 parameter, no annotations, no output schema), the description is reasonably complete: it states purpose, usage scope, and return format. However, it could improve by adding more behavioral details (e.g., error cases) since there's no output schema to define returns.

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 schema already documents the single parameter 'q' with its description. The description does not add meaning beyond this, as it does not explain parameter usage further. Baseline 3 is appropriate when schema handles 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's purpose with specific verbs ('Get timezone and current local time') and resources ('for any location'), and distinguishes it from siblings like get_current_weather or ip_lookup by focusing on timezone data rather than weather, alerts, or IP information.

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 stating it works 'for any location', but does not explicitly guide when to use this tool versus alternatives like ip_lookup (which might provide timezone via IP) or search_locations. No exclusions or specific contexts are 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 are provided, so the description carries the full burden. It discloses that the tool returns geolocation data and supports 'auto:ip' for self-geolocation, but it lacks details on behavioral traits like rate limits, authentication needs, error handling, or data freshness. The description does not contradict any annotations, but it could be more informative.

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 appropriately sized and front-loaded, with two concise sentences that directly state the tool's purpose and parameter usage. Every sentence adds essential information without redundancy, making it efficient and easy to understand.

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 somewhat complete but has gaps. It explains what the tool does and parameter usage but lacks details on output format, error cases, or limitations. Without an output schema, more information on return values would be helpful for full 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 100% description coverage, so the baseline is 3. The description adds value by explaining the semantics of the 'q' parameter beyond the schema: it clarifies that 'auto:ip' geolocates the caller's own IP, which is not obvious from the schema alone. This enhances understanding but doesn't fully compensate for missing output details.

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 with a specific verb ('Look up') and resource ('geolocation data for an IP address'), and it distinguishes itself from siblings by focusing on IP geolocation rather than weather, time, sports, or other location-based queries. The inclusion of specific data fields (city, region, etc.) further clarifies its function.

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 on when to use this tool by specifying it's for IP geolocation and mentioning the 'auto:ip' option for the caller's own IP. However, it does not explicitly state when not to use it or name alternatives among siblings, such as using other tools for non-IP-based location data.

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 does well by specifying what data is returned ('coordinates, region, country, and URL slug') and the format ('array of matching locations'), but doesn't mention important behavioral aspects like rate limits, authentication requirements, or error conditions that would be crucial for an agent.

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 perfectly structured with two sentences: the first states the core functionality and return data, the second provides practical use cases. Every word earns its place with zero redundancy or wasted space.

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 search tool with no output schema, the description does well by specifying the return format and content. However, without annotations and with no output schema, it could benefit from more detail about response structure (e.g., pagination, error cases) to be fully complete for agent use.

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 description coverage is 100%, so the input schema already documents the single parameter thoroughly. The description adds marginal value by mentioning 'partial name or postcode' and providing usage examples, but doesn't significantly enhance understanding beyond what the schema provides.

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 ('Search for cities and towns'), the resource ('locations'), and the method ('by partial name or postcode'). It distinguishes this tool from siblings like weather or timezone tools by focusing on geographic lookup rather than meteorological or temporal data.

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 about when to use this tool ('useful for building location pickers or resolving ambiguous place names'), giving practical application scenarios. However, it doesn't explicitly state when NOT to use it or mention specific alternatives among the sibling tools.

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