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 it 'Returns comprehensive information' and lists what's included (venue, events, results, competitors, regional significance), which is good behavioral context. However, it doesn't mention potential limitations like rate limits, authentication needs, error conditions, or whether it's a read-only operation (though 'Get' implies safe read).

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 well-structured: purpose statement first, then returns information, followed by args and returns sections with clear formatting. The example at the end is helpful. Some minor redundancy exists (e.g., 'detailed information' and 'comprehensive information'), but overall it's efficient with every sentence adding value.

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

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

Given 1 parameter with 0% schema coverage but an output schema exists, the description provides good context: it explains the parameter meaning with examples and outlines what information will be returned. The existence of an output schema means it doesn't need to detail return values. For a simple lookup tool with one parameter, this is reasonably complete, though it could mention if there are any prerequisites or limitations.

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 provides the parameter name 'championship_id' with clear semantics: 'Championship ID' with helpful examples ('WC2023' for World Championship 2023, 'Euro2022' for European Championship 2022). This adds meaningful context beyond the bare schema, though it doesn't specify format constraints 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 specific action ('Get detailed information') and resource ('about a specific championship by its ID'), distinguishing it from siblings like 'get_competition_by_id' by specifying it returns 'championship-specific information like regional significance' and comprehensive competition data. It explicitly mentions it's for WCA championships with examples like 'WC2023' and 'Euro2022'.

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: when you need 'detailed information about a specific championship by its ID' and want 'comprehensive information' including 'championship-specific details'. It implies usage vs. siblings by specifying championship focus, but doesn't explicitly state when not to use it or name alternatives like 'get_competition_by_id' for non-championship competitions.

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 but only states it 'returns comprehensive information' without detailing what 'comprehensive' includes, potential rate limits, authentication needs, or error handling. It adds minimal behavioral context beyond the basic 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 a clear purpose statement, bullet points for Args and Returns, and no redundant information. Every sentence adds value, 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 simplicity (1 parameter, no annotations, but has output schema), the description is mostly complete. It explains the purpose, parameter, and return intent, though it could better differentiate from siblings and add more behavioral details. The output schema reduces the need to fully describe return values.

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%, but the description compensates by explaining the parameter's purpose ('WCA competition ID') and providing an example ('e.g., "WC2023"'), adding meaningful context beyond the schema's basic string type. With only one parameter, this is sufficient.

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 verb ('Get detailed information') and resource ('about a specific competition by its ID'), distinguishing it from siblings like search_competitions_by_date or get_competition_results by focusing on a single competition's comprehensive details.

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 when detailed competition info is needed, but lacks explicit guidance on when to use this vs. alternatives like get_competition_results (for results only) or search_competitions_by_date (for multiple competitions). 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 effectively describes the tool's behavior: it returns 'focused results data' with a default round filter and optional limits/solve details. However, it lacks information on error handling, rate limits, authentication needs, or data freshness, which are important for a tool fetching competition data.

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

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

The description is well-structured and appropriately sized. It starts with a clear purpose statement, followed by behavioral notes, parameter explanations, return details, and examples. Each sentence adds value, but the parameter explanations could be slightly more concise (e.g., combining event_id examples).

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 (5 parameters, no annotations, but has an output schema), the description is mostly complete. It covers purpose, usage, parameters, returns, and examples. The output schema likely handles return structure details, so the description appropriately focuses on semantics. However, it could improve by mentioning sibling tool distinctions or error cases.

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 significant value by explaining all 5 parameters: competition_id (with examples like 'WC2025'), event_id (with event code examples), round (default and options), limit (optional max results), and include_solves (default and purpose). This goes well beyond the bare schema, though it could provide more detail on parameter constraints (e.g., format validation).

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 results for a specific event within a WCA competition.' It specifies the verb ('Get'), resource ('results'), and scope ('specific event within a competition'), distinguishing it from sibling tools like get_competition_results (which likely returns broader results) and search_competitions_by_event (which searches competitions rather than fetching results).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for usage: 'By default, returns only Final round results for better LLM processing' and explains when to use the 'round' parameter ('Use "all" for all rounds'). However, it does not explicitly state when to use this tool versus alternatives like get_competition_results or search_competitions_by_event, which limits the score to 4.

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 for behavioral disclosure. While it describes what data is returned, it doesn't mention important behavioral aspects like rate limits, authentication requirements, error conditions, pagination, or data freshness. For a data retrieval tool with no annotation coverage, this leaves significant gaps in understanding how the tool 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 well-structured with clear sections (purpose, returns, args, example) and front-loaded information. Every sentence adds value, though the 'Returns' section could be slightly more concise. The structure helps the agent quickly understand the tool's purpose and usage.

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

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

Given the tool has an output schema (which handles return value documentation) and only one parameter that's well-explained in the description, the description is reasonably complete. It covers the core purpose, parameter meaning, and provides an example. The main gap is lack of behavioral context (rate limits, errors, etc.) which would be more critical for complex tools.

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 provides excellent parameter semantics despite 0% schema description coverage. It clearly explains the 'competition_id' parameter with a definition ('WCA competition ID'), format examples ('WC2023', 'CubingUSANationals2024'), and usage in the example. This fully compensates for the lack of schema descriptions and adds meaningful context beyond what the bare 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 tool's purpose: 'Get all results for a WCA competition' with specific details about what data is returned (competitor performances, solve breakdowns, round information, final rankings). It distinguishes from siblings like 'get_competition_event_results' by specifying 'all events' rather than a single event, but doesn't explicitly contrast with other similar tools like 'get_competition_by_id'.

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 through the example and parameter explanation, showing this is for retrieving complete competition results. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_competition_by_id' (which might return metadata) or 'get_competition_event_results' (which focuses on single events). The guidance is present but not comprehensive.

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 key behavioral traits: the tool returns filtered information based on parameters, warns about potentially 'extremely long' responses with competition results, and explains default behavior. However, it lacks details on error handling, rate limits, authentication needs, 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 with a clear purpose statement, usage context, parameter explanations, and return statement. It's appropriately sized for a 7-parameter tool. Minor improvement could be front-loading the parameter section more explicitly, but overall it's efficient with minimal waste.

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 (7 parameters, no annotations, but has output schema), the description is mostly complete. It explains parameters thoroughly and mentions return behavior. The output schema existence means it doesn't need to detail return values. However, it could better address sibling tool differentiation and error cases.

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 substantial meaning beyond the input schema, which has 0% description coverage. It explains all 7 parameters with clear semantics: what each controls (e.g., 'include detailed competition results'), provides examples (e.g., '2003SEAR02'), and clarifies defaults. This fully compensates 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: 'Get detailed information about a specific WCA competitor by their WCA ID.' It specifies the verb ('Get'), resource ('WCA competitor'), and distinguishes from siblings by focusing on individual person data rather than competitions, rankings, or other entities listed in sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context through parameter explanations (e.g., 'By default, returns basic info... without verbose competition results'), but does not explicitly state when to use this tool versus alternatives like 'get_competition_results' or 'get_rankings'. It provides some guidance on avoiding overly long responses but lacks direct sibling comparisons.

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 'paginated ranking data' and mentions defaults for parameters, which adds some behavioral context. However, it doesn't cover important aspects like rate limits, authentication requirements, error conditions, or whether it's a read-only operation (though 'Get' implies reading).

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 followed by detailed parameter documentation and return information. It's appropriately sized for a tool with 5 parameters. The only minor inefficiency is that the first sentence could be more front-loaded with key details about filtering capabilities.

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 complexity (5 parameters, no annotations, but has output schema), the description is mostly complete. It explains what the tool does, documents all parameters thoroughly, and mentions the return format. The presence of an output schema means it doesn't need to detail return values. It could improve by addressing behavioral aspects like rate limits or error handling.

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 provides comprehensive parameter documentation in the 'Args' section, explaining each parameter's purpose, format, and defaults with examples. Given that schema description coverage is 0%, this description fully compensates by adding meaning beyond what the bare schema provides, making all 5 parameters well-understood.

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 current world rankings and records for a specific event.' It specifies the verb ('Get'), resource ('world rankings and records'), and scope ('for a specific event'). However, it doesn't explicitly differentiate from sibling tools like 'get_competition_event_results' or 'search_competitions_by_event', which might also involve event-related data retrieval.

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 through the mention of filtering by region and ranking type, but doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_competition_event_results' or 'search_competitions_by_event'. It lacks statements about when-not-to-use or direct comparisons with sibling tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns a list of continents with names and identifiers, which is helpful. However, it doesn't mention whether this is a read-only operation (implied by 'Get'), potential rate limits, authentication needs, or error conditions. The description adds basic behavioral context but lacks depth 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 appropriately sized at three sentences, front-loaded with the core purpose. The second sentence adds useful context about regional filtering, and the third clarifies the return structure. There's minimal redundancy, though the 'Returns:' section slightly repeats information from the second sentence.

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 (0 parameters, simple list output) and the presence of an output schema (which handles return value documentation), the description is reasonably complete. It covers the purpose, usage hint, and output format. For a straightforward read operation, this provides adequate context without over-explaining.

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

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

The tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description doesn't need to explain parameters, and it appropriately doesn't mention any. This is correct for a parameterless tool.

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 all WCA continents' with the specific verb 'Get' and resource 'WCA continents'. It distinguishes from siblings like 'get_wca_countries' by focusing on continents rather than countries. However, it doesn't explicitly differentiate from other list tools like 'get_wca_events', so it's not a perfect 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by mentioning 'regional filtering', suggesting this tool is for obtaining continent data for filtering purposes. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'get_wca_countries' or other geographic tools, nor does it specify 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 describes the return format (list with names and ISO2 codes) and hints at a read-only operation ('Get'), but does not disclose behavioral traits like rate limits, authentication needs, or potential errors. It adds basic context but lacks detailed operational guidance.

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, followed by details on returns, and structured into clear sentences without redundancy. Every sentence adds value, such as explaining the ISO2 codes' use for filtering, making it efficient and well-organized.

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

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

Given the tool's simplicity (0 parameters, output schema exists), the description is mostly complete. It explains what the tool does and the return format, but with no annotations, it could benefit from more behavioral details like error handling or data freshness. The output schema likely covers return values, so the description's focus on semantics is adequate but not exhaustive.

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 parameters with 100% coverage, so the description does not need to compensate. It appropriately focuses on output semantics, explaining the return content (list of countries with names and ISO2 codes), which adds value beyond the schema. The baseline for 0 parameters is 4, and the description meets this by clarifying the output.

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

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

The description clearly states the specific action ('Get all WCA countries') and resource ('countries recognized by the World Cube Association'), distinguishing it from siblings like get_wca_continents or get_wca_events. It explicitly mentions the return content (names and ISO2 codes), making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for obtaining country data for regional filtering, but does not explicitly state when to use this tool versus alternatives like get_wca_continents or other sibling tools. It provides clear context but lacks explicit exclusions or comparisons to other tools.

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

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

No annotations are provided, so the description carries the full burden. It mentions the return type but doesn't disclose behavioral traits like rate limits, authentication needs, or whether the data is static or frequently updated. The description is minimal beyond stating the output.

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 specifics on returns. Every sentence adds value without redundancy, making it efficiently 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 simplicity (0 parameters, output schema provided), the description is reasonably complete. It explains what the tool does and the return content, though it could benefit from more behavioral context given the lack of annotations.

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 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, earning a high baseline score for not adding unnecessary information.

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

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

The description clearly states the verb 'Get' and the resource 'all official WCA events', specifying it returns event IDs, names, and formats. It distinguishes from siblings like get_competition_event_results or get_rankings by focusing on event metadata rather than competition-specific 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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, leaving the agent to 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 that the tool returns paginated results and provides filtering capabilities, which is helpful. However, it doesn't mention important behavioral aspects like rate limits, authentication requirements, error conditions, or what happens when no championships match the filter. For a search tool with no annotation coverage, this leaves significant behavioral 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 with clear sections: purpose statement, return explanation, args documentation, returns statement, and examples. Every sentence adds value. It could be slightly more concise by combining some sentences, but overall it's efficiently organized with zero wasted text.

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

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

Given the tool has an output schema (so return values don't need explanation in description), 2 parameters with 0% schema coverage, and no annotations, the description does a good job covering the essentials. It explains the tool's purpose, parameters, and provides examples. The main gap is lack of behavioral context like error handling or performance characteristics, which would be helpful 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, so the description must fully compensate. It provides excellent parameter semantics: it explains that 'page' is for pagination with default value 1, and 'championship_type' accepts specific string values ('world', continent codes, country ISO2 codes) or null for all championships. The description adds substantial meaning beyond the bare schema, including examples of valid values and their interpretations.

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 searches for WCA championships with optional filtering and specifies the types of championships included (World, Continental, National). It distinguishes from siblings like 'search_competitions_by_date' by focusing specifically on championship competitions rather than all competitions. However, it doesn't explicitly contrast with 'get_championship_details' which retrieves details for a specific championship.

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 - when searching for championship competitions with optional filtering. The example usage demonstrates specific scenarios. However, it doesn't explicitly state when NOT to use it or when to prefer sibling tools like 'search_competitions_by_date' for non-championship competitions or 'get_championship_details' for detailed information about a specific championship.

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 useful behavioral traits: the tool returns a 'small, focused list' (1-5 competitions), which is valuable for LLM processing. However, it doesn't mention potential error conditions, rate limits, authentication needs, or what happens with invalid dates.

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 clear sections (purpose, behavior, args, returns, example) and appropriately sized. Every sentence adds value, though the example could be slightly more concise. The front-loaded purpose statement 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?

Given the tool's moderate complexity (3 parameters, no annotations, but has output schema), the description is reasonably complete. It explains the tool's purpose, parameters, return characteristics, and includes an example. The output schema existence means the description doesn't need to detail return values, which it appropriately avoids.

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 must compensate for the schema's lack of parameter documentation. It successfully adds meaning by explaining each parameter's purpose and providing valid ranges (year as integer examples, month 1-12, day 1-31), though it doesn't specify format constraints like year limits.

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 searches for WCA competitions on a specific date, using specific verbs ('search for', 'returns') and identifies the resource ('WCA competitions'). It distinguishes from siblings like 'search_competitions_by_event' by specifying date-based search rather than event-based.

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 ('on a specific date', 'exact date specified') and mentions the typical result size (1-5 competitions). However, it doesn't explicitly state when NOT to use it or name 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.

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it specifies the tool returns paginated results to manage response size, describes what the output contains ('Paginated competition data'), and mentions the default value for the page parameter. It doesn't cover rate limits or authentication needs, but provides solid 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 efficiently structured with a clear purpose statement, behavioral details, parameter explanations, return information, and an example - all in well-organized sections. Every sentence adds value with zero waste.

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, pagination), no annotations, and the presence of an output schema, the description is complete enough. It explains what the tool does, how to use it, parameter meanings, and behavioral characteristics without needing to detail return values since an output schema exists.

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: 'event_id' is described with examples of WCA event IDs, and 'page' is explained as the page number for pagination with its default value. This adds crucial 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 specific verb ('Search for') and resource ('WCA competitions') with precise scope ('that include a specific event'). It distinguishes from siblings like 'search_competitions_by_date' by specifying event-based filtering rather than date-based filtering.

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 (searching competitions by event) and includes an example. However, it doesn't explicitly mention when NOT to use it or name specific alternatives among the sibling tools, though the event-based focus is implied.

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