college-baseball

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

Annotations already indicate this is a read-only, non-destructive, idempotent, and open-world operation. The description adds context by specifying the data source (Highlightly standings data) and its conditional availability ('when available'), which provides useful behavioral insight beyond the annotations, though it doesn't detail rate limits or exact response format.

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

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

The description is a single, well-structured sentence that efficiently conveys the tool's purpose and key behavioral detail (data source availability). It is front-loaded with the main action and resource, with no wasted words or 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?

Given the tool's simplicity (0 parameters, read-only operation with annotations), the description is complete enough. It explains what the tool returns and the data source, though without an output schema, it doesn't detail the return format. For a straightforward retrieval tool, this is adequate but could be enhanced with output 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?

With 0 parameters and 100% schema description coverage, the baseline is 4. The description appropriately adds no parameter details, as none are needed, and instead focuses on the tool's function and data source, which is sufficient given the empty input 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: to retrieve a specific ranking metric (Conference Power Index) based on win percentage and run differential, using Highlightly standings data. It specifies the resource (D1 conferences) and calculation method, though it doesn't explicitly distinguish from sibling tools like 'bsi_get_rankings' or 'bsi_get_standings' beyond mentioning the data source.

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

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

No explicit guidance is provided on when to use this tool versus alternatives. The description mentions using Highlightly standings data 'when available,' which implies a dependency but doesn't clarify when to choose this over other ranking or standings tools in the sibling list.

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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds minimal behavioral context beyond this—it mentions the tool returns a 'ranked leaderboard' but doesn't specify format, pagination, or data freshness. With comprehensive annotations, the bar is lower, and the description provides some value but not rich behavioral 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 front-loaded and efficiently structured in two sentences: the first states the core action, and the second specifies the return format. Every word earns its place, with no redundancy or fluff, making it easy for an agent to parse 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 (4 parameters, no output schema) and rich annotations, the description is mostly complete. It clearly defines the tool's purpose and output format. However, it lacks guidance on usage relative to siblings and doesn't detail behavioral aspects like response structure or error handling, which would be helpful despite the 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?

Schema description coverage is 100%, with all parameters well-documented in the schema (e.g., defaults, enums, optionality). The description adds no parameter-specific semantics beyond implying ranking by 'advanced metric' and listing stat types, which the schema already covers via enum values. Baseline 3 is appropriate when the schema handles parameter documentation effectively.

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 the top college baseball hitters or pitchers by an advanced metric') and resource ('Returns a ranked leaderboard with player names, teams, and stat values'). It distinguishes itself from siblings by focusing on individual player leaderboards rather than team rankings, conference indices, or detailed match/player stats.

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 sibling tools like bsi_get_player_stats (for individual stats) or bsi_get_rankings (which might overlap), nor does it specify use cases like 'when you need top performers' versus 'when you need detailed player statistics'. The absence of contextual usage instructions leaves the agent without clear selection criteria.

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?

Annotations cover key behavioral traits (read-only, open-world, idempotent, non-destructive), so the description adds minimal value. It mentions the need for a match ID from the scoreboard, which is useful context, but does not disclose additional behaviors 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 front-loaded with the core purpose in the first sentence and includes only essential guidance in the second. Every sentence earns its place with no wasted words, 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 tool's moderate complexity (single parameter, no output schema) and rich annotations, the description is mostly complete. It covers the purpose and basic usage but lacks details on output structure or potential limitations, which could be helpful since there's no output schema.

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

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

Schema description coverage is 100%, with the parameter 'matchId' fully documented in the schema. The description adds no extra meaning beyond implying it's sourced from scoreboard results, which is already suggested by the schema's example. Baseline 3 is appropriate as the schema handles parameter semantics.

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 ('college baseball game'), listing key data points like venue, weather, and play-by-play. It distinguishes from siblings by focusing on match-level details rather than broader data like scoreboards, rankings, or team schedules.

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 ('Use a match ID from the scoreboard'), linking it to the bsi_get_scoreboard sibling. However, it does not explicitly state when not to use it or name alternatives for similar data, such as if other tools provide overlapping details.

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?

Annotations already indicate this is a safe, read-only, idempotent, and non-destructive operation (readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: true). The description adds value by specifying the data source ('BSI sabermetric data') and the types of information returned (stats, position, team, headshot), which are not covered by annotations. It does not contradict annotations, as searching aligns with read-only behavior.

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

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

The description is two concise sentences with zero waste. The first sentence front-loads the core purpose and outputs, and the second sentence specifies the data source. Every word contributes essential information without redundancy or fluff.

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), annotations cover safety and behavior well, and the description adds necessary context about data source and return types. However, without an output schema, the description could benefit from more detail on the format or scope of 'stats' returned, but it is largely complete for a search tool with good 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?

Schema description coverage is 100%, with clear descriptions for both parameters (player as required name, team as optional disambiguator). The description adds marginal context by mentioning 'search by name' and 'disambiguate when multiple players share a name', but this largely reiterates what the schema already specifies. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the specific action ('Search for a college baseball player by name'), the resource ('BSI sabermetric data'), and the outputs ('get their stats, position, team, and headshot'). It distinguishes this tool from siblings like bsi_get_team_sabermetrics or bsi_get_leaderboard by focusing on individual player data retrieval rather than team or aggregated statistics.

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

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

The description implies usage for searching college baseball players, but it does not explicitly state when to use this tool versus alternatives like bsi_get_team_sabermetrics for team-level data or bsi_get_leaderboard for rankings. It mentions disambiguation via an optional team parameter, which provides some contextual guidance but lacks explicit when-not-to-use instructions or named alternatives.

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

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

Annotations already cover key traits (read-only, open-world, idempotent, non-destructive), so the bar is lower. The description adds useful context by specifying the return format ('rank, team, record, and trend'), which is not covered by annotations, enhancing transparency about output structure.

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

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

The description is a single, efficient sentence that front-loads the purpose and includes essential details (scope and return format) without any wasted words, making it highly concise and well-structured.

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

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

Given the tool's low complexity (0 parameters, no output schema) and rich annotations, the description is nearly complete. It covers purpose and output format, but could slightly improve by mentioning data source or update frequency, though not critical 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?

With 0 parameters and 100% schema description coverage, the baseline is high. The description compensates by clarifying that no inputs are needed ('Get the latest...'), which aligns with the empty schema, adding value by confirming the tool's simplicity.

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'), resource ('latest national college baseball rankings'), and scope ('Top 25'), distinguishing it from siblings like 'bsi_get_leaderboard' or 'bsi_get_standings' by focusing on rankings rather than other statistical views.

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 retrieving Top 25 rankings, but does not explicitly state when to use this tool versus alternatives like 'bsi_get_standings' or 'bsi_get_leaderboard'. It provides basic context but lacks explicit guidance on exclusions or 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?

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds useful context beyond annotations by specifying what data is returned ('live and final games with team names, scores, venue, and game status') and the temporal scope ('today's college baseball scores'), which helps the agent understand the tool's output and limitations without contradicting annotations.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by details on returns and coverage. It uses two concise sentences with zero waste, efficiently conveying essential information without redundancy or fluff.

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) and rich annotations (covering safety and behavior), the description is mostly complete. It explains what the tool does and what data it returns, but could benefit from mentioning parameter usage or output format details to fully compensate for the lack of output schema.

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

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

Schema description coverage is 100%, so the input schema fully documents the parameters. The description does not add meaning beyond the schema, as it does not mention the optional 'date' or 'conference' parameters. Baseline score of 3 is appropriate since the schema handles parameter documentation effectively.

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 today's college baseball scores and game results') and resources ('college baseball scores and game results'), distinguishing it from siblings like bsi_get_standings or bsi_get_team_schedule by focusing on daily scores rather than standings or schedules. It specifies coverage of 'all 330 D1 teams' to define scope.

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

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

The description implies usage for retrieving daily scores, but does not explicitly state when to use this tool versus alternatives like bsi_get_match_detail for specific games or bsi_get_team_schedule for future games. It mentions coverage of all D1 teams, which provides some context but lacks clear exclusions or named alternatives.

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

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

Annotations already cover key behavioral traits (read-only, open-world, idempotent, non-destructive), so the bar is lower. The description adds context about the specific data fields returned (e.g., wins, losses, streak), which is useful beyond annotations, but it does not detail aspects like rate limits, authentication needs, or pagination behavior.

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

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

The description is a single, efficient sentence that front-loads the core purpose and lists key data fields without unnecessary words. Every part of the sentence contributes directly to understanding the tool's functionality, making it highly concise and well-structured.

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

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

Given the tool's low complexity (one optional parameter), rich annotations covering safety and behavior, and no output schema, the description is reasonably complete. It specifies the data fields returned, which compensates for the lack of output schema, though it could benefit from mentioning response format or error handling for full completeness.

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

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

Schema description coverage is 100%, so the schema already fully documents the optional 'conference' parameter. The description does not add any parameter-specific details beyond what the schema provides, such as examples or constraints, but it implies the tool can return data for all conferences if the parameter is omitted, aligning with the schema's optional nature.

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

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

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('current college baseball conference standings'), and it lists the specific data fields included (wins, losses, win percentage, etc.). This distinguishes it from siblings like bsi_get_rankings or bsi_get_leaderboard, which likely serve different statistical purposes.

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 retrieving standings data, but it does not explicitly state when to use this tool versus alternatives like bsi_get_rankings or bsi_get_leaderboard. However, the context is clear for obtaining standings, and the input schema provides optional filtering by conference, offering some guidance on scope.

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?

Annotations cover key behavioral traits (read-only, open-world, idempotent, non-destructive), so the description adds minimal value beyond stating it retrieves metrics. It does not disclose additional context like rate limits, authentication needs, or data freshness, but does not contradict annotations either.

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

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

The description is a single, efficient sentence that front-loads the purpose with specific metric examples. There is no wasted text, and it directly communicates the tool's function without unnecessary elaboration.

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) and rich annotations, the description is reasonably complete. It specifies the metrics retrieved, but could improve by mentioning the return format or data scope (e.g., season, date range). However, it adequately supports the agent in selecting the tool.

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

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

Schema description coverage is 100%, with the single parameter 'team' fully documented in the schema. The description does not add meaning beyond what the schema provides (e.g., no examples of team names beyond the schema's 'texas', 'tennessee', 'lsu'), so it meets the baseline for high schema coverage.

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

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

The description clearly states the specific action ('Get') and resource ('advanced sabermetric batting and pitching metrics for a college baseball team'), with examples of metrics (wOBA, wRC+, FIP, etc.) that distinguish it from sibling tools like bsi_get_player_stats or bsi_get_standings. It precisely communicates what the tool does without being vague or tautological.

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 team-level sabermetrics, but does not explicitly state when to use this tool versus alternatives like bsi_get_player_stats (for individual players) or bsi_get_leaderboard (for comparisons). It provides context (college baseball team metrics) but lacks explicit guidance on exclusions or prerequisites.

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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds value by specifying the scope ('full schedule... including past results and upcoming games'), but doesn't disclose additional behavioral traits like rate limits, authentication needs, or pagination, which could be relevant given the open-world hint.

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

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

The description is a single, efficient sentence that front-loads the key information ('Get the full schedule...') with no wasted words. It directly communicates the tool's purpose and scope, making it easy to parse 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 low complexity (1 parameter, no output schema) and rich annotations covering safety and idempotency, the description is mostly complete. It specifies what data is returned (schedule with past and upcoming games), though it could benefit from mentioning output format or any limitations, but this is mitigated by the annotations providing key behavioral 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 input schema has 100% description coverage, with the 'team' parameter clearly documented as 'Team name or slug'. The description doesn't add any extra meaning beyond this, such as examples of valid teams or formatting details, so it meets the baseline for high schema coverage without compensating further.

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 action ('Get') and resource ('full schedule for a college baseball team'), specifying it includes both past results and upcoming games. It distinguishes this tool from siblings like 'bsi_get_standings' or 'bsi_get_scoreboard' by focusing on a team-specific schedule, though it doesn't explicitly mention how it differs from 'bsi_get_match_detail' which might cover individual games.

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 retrieving a team's schedule, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'bsi_get_scoreboard' for broader game listings or 'bsi_get_standings' for team rankings. No exclusions or prerequisites are mentioned, leaving some ambiguity in context.

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