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

With no annotations provided, the description carries full burden. It describes what information is returned (history, statistics, route) and mentions filtering by classification, but lacks critical behavioral details: whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or pagination behavior. The description covers basic functionality but misses important 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 well-structured with purpose statement, usage note, examples, and return format. While slightly verbose, each section adds value. The front-loaded purpose statement is clear, and the examples are helpful. Minor trimming could improve conciseness without losing 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 2 parameters with 0% schema coverage and no output schema, the description provides adequate context for basic usage but has gaps. It explains the return format (formatted string with specific sections) which compensates for missing output schema, but doesn't fully document parameter semantics or behavioral traits. For a read operation with filtering, this is minimally complete but could be more comprehensive.

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 0%, so the description must compensate. It explains that race_id identifies a specific race and mentions filtering by classification (mapping to classification_num), but doesn't specify valid ranges, formats, or examples for classification_num. The description adds some meaning but doesn't fully document both parameters, especially classification_num's purpose and constraints.

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 comprehensive details about a cycling race' with specific information types (history, statistics, route). It distinguishes from siblings like search_race (which finds races) and get_race_edition_results (which focuses on results). However, it doesn't explicitly differentiate from all siblings like get_race_victory_table.

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 explicit guidance: 'If you don't know the race's ID, use the search_race tool first to find it by name.' This clearly states when to use an alternative tool (search_race) and establishes a prerequisite workflow.

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 adequately describes what the tool returns (formatted string with race details, results, rider info) but doesn't mention important behavioral aspects like rate limits, error conditions, authentication requirements, or whether results are cached/live. The description doesn't contradict annotations (none exist), but leaves significant behavioral context unspecified.

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 well-structured and appropriately sized: it starts with the core purpose, adds filtering context, provides prerequisite guidance, gives concrete examples, and details the return format. Every sentence adds value with zero 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 tool with 4 parameters, 0% schema coverage, no annotations, and no output schema, the description does a good job explaining the tool's purpose, usage, and return format. However, it doesn't fully compensate for the lack of behavioral context (rate limits, errors, etc.) or detailed parameter semantics (what classification numbers mean, valid ranges).

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 0% schema description coverage (titles only provide parameter names), the description must compensate. It effectively explains the purpose of race_id and year through examples, clarifies that classification_num and stage_num are optional filters, and shows how they work together. However, it doesn't explain what classification_num values represent or provide format details for any parameters.

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 verb ('Get detailed results') and resource ('for a specific edition of a cycling race'), distinguishing it from sibling tools like get_race_details (which likely provides metadata) or search_race (which finds races). It explicitly mentions what results include: rankings, time gaps, and 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 provides explicit guidance on when to use this tool vs. alternatives: it instructs to use search_race first if the race ID is unknown, and mentions filtering by classification or stage. It also gives three concrete example use cases that illustrate different parameter combinations.

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 what the tool returns ('a formatted string with: race name, list of riders with the most victories, number of victories for each rider, years of victories where available'), which is crucial since there's no output schema. However, it doesn't mention potential limitations like data availability for all races or pagination for large result sets, leaving some behavioral aspects unspecified.

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: the first sentence states the core purpose, followed by elaboration, usage note, examples, and return format. Every sentence adds value—none are redundant or wasteful—making it efficient and well-structured for quick understanding.

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), no annotations, no output schema, and low schema coverage, the description does a good job of covering key aspects: purpose, usage guidelines, parameter context, and return format. However, it lacks details on error handling or data completeness (e.g., what happens if no victory data exists), which would enhance completeness for this context.

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 0%, so the description must compensate. It adds meaningful context for the single parameter (race_id) by explaining it's needed to identify the race and providing examples (Tour de France ID: 17, Paris-Roubaix ID: 30), which clarifies the parameter's purpose beyond the schema's basic type definition. However, it doesn't specify where to find these IDs or if they're standardized, leaving some ambiguity.

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 the all-time victory table') and resource ('for a cycling race'), distinguishing it from sibling tools like get_race_details or get_race_edition_results by focusing on historical rider victories rather than race details or specific edition results. It explicitly mentions what the tool provides: 'a historical summary of the most successful riders in a specific race, showing the number of victories for each rider throughout the race's history.'

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 includes explicit guidance on when to use this tool versus alternatives: 'If you don't know the race's ID, use the search_race tool first to find it by name.' This directly addresses a common prerequisite and names the specific sibling tool to use as an alternative, providing clear context for proper 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 full burden. It discloses that results are 'sorted by importance' and include 'detailed information about each race,' which adds behavioral context beyond basic retrieval. However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions, leaving some behavioral aspects unclear.

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 well-structured with a clear purpose statement, usage note, examples, and return format details. Each sentence adds value, but it could be slightly more concise by integrating the return format into the initial overview without a separate bulleted list.

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 does a good job covering purpose, usage, parameters, and return format. It provides enough context for an agent to use the tool effectively, though it could benefit from mentioning any prerequisites or error handling to be fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explains that 'rider_id' identifies the cyclist and provides example IDs, and implies 'limit' controls the number of results returned (e.g., 'top 10' or 'top 5'). This adds meaningful context beyond the bare schema, though it doesn't detail parameter constraints or formats explicitly.

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 ('retrieve'), resource ('best career results of a professional cyclist'), and scope ('including top finishes, stage wins, overall classifications'). It distinguishes from siblings like 'get_rider_info' or 'get_rider_victories' by focusing on comprehensive best achievements rather than general info or just wins.

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 explicitly provides when-to-use guidance: 'If you don't know the rider's ID, use the search_rider tool first to find it by name.' It also implies usage context through example queries, helping differentiate from sibling tools that might return different data types or scopes.

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 that the tool returns 'detailed information' and a 'formatted string' with specific content (overall classification, stage wins, etc.), and mentions data is 'organized chronologically.' However, it doesn't cover potential limitations like rate limits, error conditions, or authentication needs, leaving some behavioral aspects unspecified.

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 well-structured and front-loaded with the core purpose, followed by usage notes, examples, and return details. Every sentence adds value—no redundancy or fluff—making it efficient and easy to parse.

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 single parameter, lack of annotations, and no output schema, the description does a solid job by covering purpose, usage, parameter context, and return format. However, it could be more complete by mentioning any data recency limits or access constraints, which are relevant for a data-fetching 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 input schema has 0% description coverage, but the description compensates by explaining that the rider_id parameter should be obtained via search_rider if unknown, and provides example IDs (e.g., 16973 for Tadej Pogačar). This adds meaningful context beyond the bare schema, though it doesn't detail ID format or validation rules.

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 comprehensive results'), resource ('rider in Grand Tours'), and scope ('Tour de France, Giro d'Italia, and Vuelta a España'), distinguishing it from sibling tools like get_rider_monument_results or get_rider_one_day_races by focusing exclusively on three-week races.

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?

Explicit guidance is provided: 'If you don't know the rider's ID, use the search_rider tool first to find it by name.' This directly addresses a common prerequisite and names the alternative tool, giving clear when-to-use instructions.

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 that the tool returns 'comprehensive information' and lists specific data points (e.g., team, nationality, recent results), which helps set expectations. However, it lacks details on potential limitations (e.g., data freshness, error handling, or rate limits), leaving behavioral gaps for a tool with no annotation support.

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 well-structured and front-loaded, starting with the core purpose. It uses bullet points for returns and examples efficiently, but includes some redundancy (e.g., repeating 'basic info' in examples when the description already states 'comprehensive information'). Overall, most sentences add value without excessive verbosity.

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, no output schema, and low schema coverage, the description does a good job by detailing the return format (formatted string with specific data points) and providing usage examples. It covers key aspects like purpose, parameters, and outputs, though it could improve by addressing potential errors or data scope limitations for better 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 0% description coverage (only 'rider_id' with type integer), so the description must compensate. It adds meaning by explaining that rider_id is required and provides examples (e.g., Tadej Pogačar ID: 16973), clarifying the parameter's purpose. However, it doesn't specify format constraints or sourcing details beyond the examples.

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 comprehensive information about a professional cyclist' with specific details like current team, nationality, date of birth, and recent race results. It distinguishes itself from siblings by focusing on a comprehensive overview rather than specialized queries (e.g., get_rider_victories or get_rider_race_history).

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 explicit guidance: 'If you don't know the rider's ID, use the search_rider tool first to find it by name.' This clearly indicates when to use this tool (with a known ID) versus an alternative (search_rider for unknown IDs), addressing a key usage scenario.

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 what information will be returned (positions, times, achievements, chronological organization) and the output format (formatted string), but doesn't mention error handling, rate limits, or authentication requirements that would be helpful for a 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?

The description is well-structured with purpose, context, prerequisites, examples, and return format. While slightly longer than minimal, every sentence adds value. The front-loading of the core purpose followed by practical guidance is effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter read tool with no annotations or output schema, the description provides substantial context: clear purpose, usage prerequisites, examples, and detailed return format. The main gap is lack of error case information, but otherwise it's quite complete for this complexity level.

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 0% schema description coverage for the single parameter (rider_id), the description compensates by explaining what the parameter represents (rider's ID) and providing guidance on how to obtain it (use search_rider tool). The examples also demonstrate proper usage with specific rider IDs.

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: 'Retrieve detailed results for a rider in cycling's five Monument races' with specific resources (rider's performance data) and scope (five named races). It distinguishes from siblings like get_rider_grand_tour_results by focusing exclusively on Monument races.

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 explicit guidance on when to use this tool ('If you don't know the rider's ID, use the search_rider tool first') and includes two concrete usage examples. It clearly positions this tool for Monument-specific results versus other rider result 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adequately describes the tool's function and output format (a formatted string with organized results), but lacks details on potential limitations, error handling, or performance aspects like rate limits. It doesn't contradict annotations, but could be more comprehensive for a tool with no structured behavioral hints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded, starting with the core purpose, followed by details, usage notes, examples, and return format. Every sentence adds value without redundancy, and it's appropriately sized for the tool's complexity, making it easy to scan and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is largely complete. It covers purpose, usage, parameters, and output format adequately. However, it could improve by mentioning any constraints on the 'year' parameter or error cases, but overall it provides sufficient context for an agent to use the tool effectively.

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 0%, so the description must compensate. It effectively explains the semantics of both parameters: 'rider_id' is required for identifying the rider, with a note to use 'search_rider' if unknown, and 'year' is optional for filtering results. This adds meaningful context beyond the bare schema, though it doesn't specify format constraints or examples for the parameters.

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', 'retrieves') and resources ('rider's results in one-day races'), distinguishing it from siblings like 'get_rider_grand_tour_results' or 'get_rider_stage_races' by focusing exclusively on one-day races. It explicitly mentions the type of races (classics and one-day events) and the data provided (positions, times, race categories).

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 explicit guidance on when to use this tool vs. alternatives, including a prerequisite note to use 'search_rider' if the rider ID is unknown, and it distinguishes usage from other tools by specifying it's for one-day races only. The example usage further clarifies typical scenarios, making it easy for an agent to determine applicability.

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 adequately describes the retrieval operation and output format, but lacks details about error handling, rate limits, authentication requirements, or data freshness. It doesn't contradict any annotations since none exist.

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 well-structured with purpose, usage note, examples, and return format sections. While slightly verbose, each sentence adds value. It could be more concise by combining some details about the return 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 2-parameter tool with no annotations and no output schema, the description provides good coverage: purpose, parameters, usage guidance, examples, and return format. It adequately compensates for the lack of structured metadata, though could benefit from more behavioral details.

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 description adds meaningful context beyond the schema's 0% coverage. It explains that rider_id identifies the cyclist and year is an optional filter, and provides concrete examples with specific IDs (16973, 16974). This compensates well for the schema's lack of descriptions.

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', 'retrieves') and resource ('complete race history of a professional cyclist'). It distinguishes from siblings by focusing on comprehensive historical data rather than specific result types (e.g., best results, victories) or other rider 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 provides explicit guidance on when to use this tool versus alternatives: it specifies to use search_rider first if the rider ID is unknown, and mentions optional year filtering. This directly addresses a key usage scenario and references a sibling tool.

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 adequately describes the tool's function (retrieving detailed performance data) and output format (formatted string with organized results), but lacks information on potential limitations like rate limits, error conditions, or data freshness. It doesn't contradict any annotations since none exist.

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 well-structured and front-loaded with the core purpose, followed by details and examples. While slightly verbose in listing return format details, every sentence adds value (e.g., distinguishing stage races, providing usage notes, and examples). It could be more concise by combining some return format details.

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 does a good job explaining the tool's purpose, usage, parameters, and return format. It covers the essential context for a read-only query tool with two parameters. Minor gaps include lack of error handling or performance characteristics, but it's largely complete for its complexity.

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 0%, so the description must compensate. It explains that rider_id identifies the rider and year provides optional filtering, adding meaningful context beyond the bare schema. However, it doesn't specify format constraints (e.g., valid year ranges) or provide examples of invalid inputs.

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 a rider's results in stage races') and resource ('stage races'), distinguishing it from siblings like get_rider_one_day_races and get_rider_grand_tour_results by specifying multi-day races like Tour de France and Giro d'Italia. It explicitly mentions what data is retrieved (positions, times, race categories).

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 explicit guidance on when to use this tool vs alternatives: it specifies to use search_rider first if the rider ID is unknown, and it distinguishes from other tools by focusing on stage races. The examples reinforce proper usage with specific rider IDs and filtering by year.

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 the tool returns (formatted string with team history, ranking positions, timeline, current status) but lacks details on error handling, rate limits, authentication needs, or data freshness. It adequately covers the output format but misses operational constraints.

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 well-structured with a clear purpose statement, usage note, examples, and return format. It's slightly verbose but each section adds value. The front-loaded purpose is effective, though the 'Returns' section could be more 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 annotations, 1 parameter with 0% schema coverage, and no output schema, the description does a good job covering purpose, usage, parameter meaning, and output format. It lacks details on errors or constraints, but for a read-only lookup tool, it provides sufficient context 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 0% description coverage, but the description compensates by explaining the parameter's meaning: 'rider's ID' is required, and examples show it's a numeric identifier (e.g., 16973 for Tadej Pogačar). This adds crucial context beyond the bare schema, though it doesn't detail ID format or sourcing beyond the search_rider reference.

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 information', 'retrieves', 'provides') and resources ('professional cyclist's team affiliations and UCI rankings', 'team history', 'ranking points over time'). It distinguishes from siblings like get_rider_info or get_rider_teams by emphasizing the combined career progression focus.

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 explicitly provides when-to-use guidance: 'If you don't know the rider's ID, use the search_rider tool first to find it by name.' It names the alternative tool (search_rider) and specifies the prerequisite condition, offering clear operational 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adequately describes the tool's function and output format, but doesn't mention potential limitations like rate limits, authentication requirements, error conditions, or data freshness. It provides basic behavioral context but lacks operational details.

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 with purpose statement, usage note, examples, and return format - each section earns its place. It's front-loaded with the core functionality and avoids unnecessary verbosity while covering all essential aspects.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter read operation with no output schema, the description provides good completeness: it explains the tool's purpose, parameter semantics, usage workflow, and output format. The main gap is lack of operational constraints (rate limits, errors, etc.), but given the tool's relative simplicity and clear sibling relationships, it's mostly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage (just 'Rider Id' with type integer), so the description must fully compensate. It clearly explains that the rider_id parameter should be obtained from the search_rider tool and provides concrete examples with sample IDs (12345, 67890), adding substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Get a detailed history', 'provides a chronological list') and resources ('professional cyclist's team affiliations', 'all teams the rider has been part of'). It distinguishes from siblings by focusing specifically on team history rather than race results, victories, or general rider info.

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 explicit guidance on when to use this tool versus alternatives: it directs users to 'use the search_rider tool first to find [the rider's ID] by name' if they don't know the ID. This creates a clear workflow relationship with a specific sibling tool.

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 what the tool does (retrieves detailed victory information), mentions filtering options (WorldTour only), and outlines the return format (formatted string with specific details). However, it lacks information on potential limitations like rate limits, 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 well-structured and front-loaded: it starts with the core purpose, adds filtering details, provides usage notes, includes practical examples, and ends with return value information. Every sentence adds value without redundancy, making it efficient and easy to parse.

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 complexity (2 parameters, no output schema, no annotations), the description does an excellent job covering purpose, usage, parameters, and return format. It lacks only minor details like error cases or pagination, but for a read-only query tool, it is nearly complete. The absence of an output schema is mitigated by the detailed return description.

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 0%, so the description must compensate. It fully explains both parameters: rider_id is described in the context of retrieving victories for a specific cyclist, and world_tour_only is explicitly mentioned as a filter option. The example usage further clarifies how these parameters are applied.

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 ('retrieves detailed information about all UCI-registered race victories') and distinguishes it from siblings by focusing on comprehensive victory data rather than specific race types (e.g., grand_tour_results, monument_results) or basic rider info. It explicitly mentions filtering by WorldTour wins, which adds specificity.

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 explicit guidance on when to use this tool vs. alternatives: it instructs to use search_rider first if the rider ID is unknown, and it distinguishes from siblings by specifying that this tool is for 'comprehensive list of UCI victories' rather than subsets like best_results or race_history. The example usage reinforces this 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool returns (formatted string with results, positions, categories, chronological organization) and implies it's a read-only operation, but lacks details on error handling, rate limits, authentication needs, or data freshness. It adds some value but is incomplete 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 well-structured and front-loaded with the core purpose, followed by usage notes, examples, and return details. Every sentence adds value without redundancy, and the length is appropriate for the tool's complexity.

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, 0% schema coverage, and no output schema, the description does a good job explaining purpose, parameters, and returns. However, it lacks details on error cases (e.g., invalid IDs/years), performance limits, or data sources, leaving some gaps for a tool with rich output.

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 0%, so the description must compensate. It explains that 'rider_id' corresponds to a professional cyclist's identifier (with examples like '16973' for Tadej Pogačar) and 'year' is a calendar year for results (e.g., 2023). This adds meaningful context beyond the bare schema, though it doesn't detail format constraints or validation rules.

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 ('retrieve detailed results'), resource ('professional cyclist'), and scope ('for a specific year'). It distinguishes from sibling tools by focusing on comprehensive yearly performance data rather than specific race types (e.g., 'get_rider_grand_tour_results') or general rider information ('get_rider_info').

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 explicitly provides when-to-use guidance by stating 'If you don't know the rider's ID, use the search_rider tool first to find it by name,' naming an alternative tool. It also implies usage context through example scenarios (e.g., 'Get 2023 results for Tadej Pogačar'), though it doesn't explicitly exclude other use cases.

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 adequately describes the tool's function and output format, but doesn't address potential behavioral aspects like error conditions (e.g., what happens with invalid race IDs), rate limits, authentication requirements, or whether the operation is read-only (though 'Get' implies it).

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 with purpose statement, important notes, usage examples, and return format - all in well-organized paragraphs. Every sentence adds value without redundancy, and critical information (like the search_race prerequisite) 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 2-parameter tool with no annotations and no output schema, the description provides excellent coverage of purpose, usage, parameters, and return format. The only minor gap is lack of explicit mention about the tool being read-only or any error handling, but given the clear 'Get' verb and detailed examples, it's largely complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by explaining both parameters: 'race_id' is required and identifies the specific race edition, while 'year' is optional with default behavior (current year if unspecified). The examples further clarify parameter usage with concrete values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verb ('Get') and resource ('start list for a specific edition of a cycling race'), and distinguishes it from siblings by focusing on rider/team participation data rather than race results, rider history, or rankings. It explicitly mentions what the start list includes (rider numbers, names, and teams).

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 explicit guidance on when to use this tool vs alternatives: it directs users to use 'search_race' first if they don't know the race ID, and clarifies that if no year is specified, the current year will be used. This addresses both prerequisites and parameter defaults.

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 mentions the tool 'returns a formatted string' with details like ranking list and filtering, which adds some behavioral context. However, it doesn't disclose critical traits such as whether this is a read-only operation, rate limits, authentication needs, or pagination behavior (implied by 'page_num' parameter but not explained).

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, starting with a clear purpose statement. The example usage and return format sections are useful but could be more concise. Overall, most sentences earn their place, though the return format description is somewhat repetitive (e.g., 'Filtered by specified categories' could be integrated).

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 5 parameters with 0% schema coverage and no output schema, the description provides basic purpose and examples but lacks completeness. It doesn't fully explain parameter semantics, behavioral traits (e.g., pagination, errors), or output details beyond a vague 'formatted string'. For a tool with this complexity, it should do more to compensate for missing structured data.

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 0%, so the description must compensate. It mentions filtering by 'ranking type, year, and category' and provides examples that hint at parameters (e.g., 'World UCI rider rankings for 2023'), but doesn't fully explain all 5 parameters (category, country_code, page_num, rank_type, year) or their semantics (e.g., what values rank_type accepts). The description adds some meaning but insufficiently covers the parameters.

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 'gets UCI rankings for riders, teams, or nations' with specific resources (riders/teams/nations) and action (get rankings). It distinguishes from siblings by focusing on rankings rather than race details, rider histories, or search functions. However, it doesn't explicitly contrast with 'get_rider_team_and_ranking' which might overlap.

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 implied usage through examples (e.g., 'Get World UCI rider rankings for 2023'), suggesting when to use this tool for ranking data. However, it lacks explicit guidance on when to choose this over alternatives like 'get_rider_team_and_ranking' or other sibling tools, and doesn't mention prerequisites or exclusions.

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 that the tool returns a list of matching races with IDs and countries, and mentions the output format. However, it doesn't cover important behavioral aspects like pagination, rate limits, error conditions, or whether the search is case-sensitive/fuzzy. The example usage adds some context but leaves gaps.

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 well-structured and front-loaded: purpose first, then utility, examples, and return format. Every sentence earns its place - no redundant information. The example usage is helpful without being verbose.

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 search), no annotations, and no output schema, the description does a good job covering purpose, usage, parameter meaning, and return format. However, it lacks details about search behavior (exact vs. partial matching, sorting) and error handling that would make it fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage (no parameter descriptions in schema), but the description compensates well. It explains that the 'query' parameter is for searching by race name, provides two concrete examples ('tour' and 'giro'), and clarifies this is for name-based search. This adds significant meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Search for cycling races by name' (specific verb+resource). It distinguishes from siblings by focusing on name-based search rather than retrieving details, results, or rider information like get_race_details or search_rider. The description explicitly mentions what it returns and why it's useful.

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: 'useful when you know a race's name but need its ID for other operations,' which implicitly suggests using this tool for ID lookup rather than get_race_details for comprehensive race information. However, it doesn't explicitly state when NOT to use this tool or name specific alternatives among the 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 the full burden of behavioral disclosure. It effectively describes the tool's behavior: it performs a search operation (implying read-only, non-destructive), returns a list of matches with specific fields (IDs, names, nationality, team), and indicates the output format. However, it lacks details on error handling, rate limits, or authentication needs.

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 well-structured and front-loaded with the core purpose, followed by usage guidance, examples, and output details. Each sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 output schema, no annotations), the description is largely complete. It covers purpose, usage, parameters, and output format adequately. However, it could improve by mentioning limitations (e.g., partial name matching) or error cases, which would enhance completeness for a search 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 schema has 0% description coverage for its single parameter (query), but the description compensates by explaining that the query is used to 'search for professional cyclists by name' and provides examples (e.g., 'Tadej Pogacar', 'Van Aert'). This adds meaningful context beyond the bare schema, though it does not specify format constraints like case sensitivity.

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 ('search') and resource ('professional cyclists by name'), distinguishing it from sibling tools that focus on retrieving specific rider data (e.g., get_rider_info) or race-related information. It explicitly mentions returning IDs and basic information, which sets it apart as a 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 explicit guidance on when to use this tool ('when you need a rider's ID for other operations but only know their name'), including example use cases. It implicitly distinguishes it from sibling tools by focusing on name-based searching rather than retrieving pre-defined rider data, though it does not explicitly name alternatives.

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