college-baseball

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds context: how the index is computed (win% + run-differential), exclusions (single-team and zero-game conferences), and that it's not authoritative. No contradictions.

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?

Three sentences: purpose+methodology, limitations, data sources. Every sentence adds value; no wasted words. Front-loaded with the tool's core action.

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?

With no parameters, no output schema, and all annotations provided, the description fully explains what the tool does, how it computes, exclusions, and limitations. No gaps for an agent.

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

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

No parameters exist, so schema coverage is 100% (empty schema). Description adds meaning about the output and usage, but no parameter details are needed. Baseline 4 for zero parameters is appropriate.

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

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

The description clearly states the tool gets a 'naive Conference Power Index' from standings, with explicit exclusions and a specific verb ('Get'). It distinguishes from siblings like rankings and standings by specifying it's a rough sort based on win% and run-differential.

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?

It says when to use (rough conference ranking) and explicitly notes it's not strength-of-schedule weighted, indicating when not to rely on it for authoritative rankings. It also mentions data source priority. Slightly more explicit alternatives could be given, but still strong guidance.

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 declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds that it returns both live and final games with specific fields (team names, scores, venue, status), which is consistent and provides additional behavioral context beyond 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?

Two sentences, no redundancy, front-loaded with purpose. Every sentence adds value: first states core function, second details returned data.

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?

No output schema, but description sufficiently describes return fields. Covers scope (today's, college baseball, all Division I). Could mention date parameter flexibility, but overall adequate for selection.

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 provides 100% coverage for both parameters (date, conference) with descriptions. Description does not add further meaning to parameters beyond what schema offers, meeting baseline but not exceeding.

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?

Description clearly states 'Get today's college baseball scores and game results', specifying verb, resource, and scope. It distinguishes from siblings like bsi_get_match_detail (individual game details) and bsi_get_team_schedule (scheduled games), covering all NCAA Division I programs.

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?

Description indicates it returns live/final games with team names, scores, venue, and status, but does not explicitly specify when to use this over siblings or provide exclusion criteria. Context from sibling names suggests broader overview vs. detailed tools, but no direct guidance.

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.

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. The description adds useful context: defaults to trusted college-baseball domains, provider switching (Tavily/Exa), and that it complements stats tools. No contradictions.

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?

Three sentences: purpose, usage guidance, and provider/defaults. Every sentence is necessary and front-loaded. No redundancy or 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 no output schema, the description covers the core search behavior, defaults, and provider. It could mention result format or limitations but is largely complete for a straightforward 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?

Schema coverage is 100% so all parameters have descriptions. The description reinforces domain default behavior but does not add new meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the verb 'Search' and the resource 'open web for college-baseball news, scouting reports, beat-writer coverage, and analytical commentary'. It explicitly distinguishes from sibling stats tools by noting it provides narrative context, not just numbers.

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?

Provides explicit when-to-use ('when a question needs narrative context, not just numbers') and implies alternatives (stats tools for numbers). Also clarifies default domain behavior and how to search the full open web (pass []).

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