college-baseball

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

Annotations cover key behavioral traits: read-only, open-world, idempotent, and non-destructive. The description adds value by specifying the type of data returned ('box score, scoring summary, and team stats'), which isn't in the annotations. However, it doesn't disclose additional context like rate limits, authentication needs, or error conditions. No contradiction with annotations exists.

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 ('Get detailed college football game information') and elaborates with specific details. There's no wasted verbiage, and it's appropriately sized for a simple lookup tool with one parameter.

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 required parameter), rich annotations (covering safety and behavior), and lack of output schema, the description is reasonably complete. It specifies the data returned, which compensates for the missing output schema. However, it could improve by mentioning data recency or limitations (e.g., historical vs. live games).

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 'gameId' fully documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides (e.g., format examples or sourcing details). Baseline score of 3 is appropriate as the schema carries the full burden.

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 resource ('detailed college football game information') with specific content details ('box score, scoring summary, and team stats'). It distinguishes from siblings like 'bsi_get_cfb_scoreboard' (which lists games) and 'bsi_get_cfb_team' (which focuses on teams), but doesn't explicitly contrast them. The purpose is specific and actionable.

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 (e.g., needing a game ID from another source like 'bsi_get_cfb_scoreboard'), nor does it differentiate from similar tools like 'bsi_get_match_detail' or other sport-specific game tools. Usage is implied by the name and description but not explicitly stated.

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 value by specifying the return format (rank, team, record, trend) and clarifying it provides the 'latest' data, which is useful context beyond the annotations. No contradictions with annotations exist.

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

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

The description is a single, well-structured sentence that front-loads the purpose and includes essential details (polls, return data). Every word earns its place, with no redundancy or fluff, making it highly 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 simplicity (0 parameters, no output schema) and rich annotations, the description is largely complete. It covers purpose, return data, and recency. A minor gap is the lack of explicit usage guidance versus siblings, but overall it provides sufficient context for effective tool selection and invocation.

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?

There are 0 parameters, with 100% schema description coverage. The description does not need to explain parameters, and it appropriately omits parameter details. A baseline of 4 is applied as it efficiently handles the no-parameter case without 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 'latest college football rankings', specifying the polls included (AP Top 25, Coaches Poll, CFP) and the data returned (rank, team, record, trend). It distinguishes from siblings like bsi_get_cfb_standings by focusing on rankings rather than standings, and from bsi_get_rankings by specifying college football.

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 needing rankings data, but does not explicitly state when to use this tool versus alternatives like bsi_get_cfb_standings or bsi_get_rankings. No exclusions or prerequisites are mentioned, leaving the agent to infer context from the tool name and description.

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 read-only, non-destructive, idempotent, and open-world operation. The description adds useful context about data sources (ESPN primary with SportsDataIO fallback) and the types of games returned (live, upcoming, final), which helps set expectations. However, it does not disclose additional behavioral traits like rate limits, authentication needs, or response format 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 with the core purpose in the first sentence, followed by details on return data and sources. It is appropriately sized with two concise sentences that each add value, with no wasted words or redundancy.

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 mostly complete. It specifies the sport, date scope, data returned, and sources, but could improve by mentioning the lack of output schema or example response structure, though annotations provide good 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 'date' parameter fully documented in the schema. The description mentions 'for a given date' but does not add meaning beyond what the schema provides, such as date format or default behavior. Baseline 3 is appropriate as 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 specific action ('Get college football (FBS) scores and game results') and resource ('for a given date'), distinguishing it from siblings like bsi_get_cfb_game (single game) and bsi_get_cfb_standings (standings data). It specifies the scope (live, upcoming, final games) and data sources (ESPN with SportsDataIO fallback).

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: to retrieve scores and game results for college football on a specific date. It implies usage for date-based queries but does not explicitly state when not to use it or name alternatives among siblings, though the tool name suggests it's for scoreboard data versus other football 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?

Annotations cover key behavioral traits (read-only, open-world, idempotent, non-destructive), so the description's burden is lower. It adds context by specifying the data included (wins, losses, etc.) and the scope ('by conference'), but does not disclose additional behaviors like rate limits, authentication needs, or data freshness. No contradiction with annotations exists.

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 includes relevant details (e.g., data fields). There is no wasted verbiage, and it is appropriately sized for a no-parameter tool.

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 (simple read operation with no parameters) and rich annotations, the description is adequate but minimal. It lacks output schema, so it does not explain return values or format, and it does not address sibling tool differentiation, which could aid the agent in 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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the tool's output semantics, which is sufficient given the empty 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: 'Get current college football standings by conference including wins, losses, win percentage, and conference records.' It specifies the verb ('Get'), resource ('college football standings'), and scope ('by conference'), but does not explicitly differentiate from sibling tools like 'bsi_get_standings' or 'bsi_get_cfb_rankings', which may have overlapping domains.

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

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

No guidance is provided on when to use this tool versus alternatives. The description does not mention prerequisites, exclusions, or comparisons with sibling tools such as 'bsi_get_cfb_rankings' or 'bsi_get_standings', leaving the agent to infer usage 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 this as read-only, open-world, idempotent, and non-destructive, covering key behavioral traits. The description adds context about what data is returned (record, venue, roster), which is useful but doesn't elaborate on rate limits, authentication needs, or response format. No contradiction with 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 a single, efficient sentence that front-loads the purpose and key details (team detail components and identifier). There's no wasted text, 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 low complexity (1 parameter, no output schema) and rich annotations, the description is mostly complete. It specifies the data returned but could benefit from mentioning the lack of output schema or potential response structure. However, it adequately covers the core functionality for a read-only lookup 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 parameter 'teamId' fully documented in the schema (including examples). The description doesn't add any parameter details beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without extra value.

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 resource 'college football team detail' with specific components (record, venue, roster), and distinguishes from siblings by focusing on team details rather than games, rankings, or other sports data. It specifies the domain (college football) and identifier type (ESPN team 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 implicitly indicates when to use this tool—when you need detailed team information for a specific college football team identified by ESPN ID. However, it doesn't explicitly state when not to use it or name alternatives among the many sibling tools (e.g., bsi_get_cfb_standings for standings instead of team 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 cover key behavioral traits (read-only, open-world, idempotent, non-destructive), so the description's burden is lower. It adds value by specifying 'Updated daily,' which provides context on data freshness—a useful detail not captured in annotations. No contradictions with annotations are present, and the description complements them with practical information.

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 highly concise and front-loaded: two sentences that directly state the tool's purpose and a key behavioral note ('Updated daily'). Every word earns its place, with no wasted information, making it efficient and easy for an agent 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 simplicity (0 parameters, rich annotations covering safety and behavior), the description is reasonably complete. It explains what data is fetched and its update frequency. However, without an output schema, it doesn't detail the return format (e.g., structure of entries/commitments), which could be helpful for an agent. The annotations and lack of parameters reduce the need for extensive description, but a minor gap remains.

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 doesn't add parameter details, which is fine here. A baseline of 4 is appropriate since the schema fully handles parameters, and the description doesn't need to compensate for any gaps.

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 the latest college football transfer portal entries and commitments.' It specifies the verb ('Get') and resource ('college football transfer portal entries and commitments'), making it easy to understand what data is retrieved. However, it doesn't explicitly differentiate from sibling tools like 'bsi_get_cfb_team' or 'bsi_get_player_stats', which might also provide related player data, so it falls short of a perfect score.

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 some context with 'Updated daily,' implying this tool is for current data, which helps guide usage timing. However, it doesn't offer explicit guidance on when to use this tool versus alternatives (e.g., compared to 'bsi_get_cfb_team' for team-specific transfers or 'bsi_search_intel' for broader searches). The usage is implied but not clearly articulated, leaving gaps for an agent to infer.

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 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 declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds value by specifying the types of information returned (box score, play-by-play, team stats), which helps set expectations beyond the schema. However, it doesn't disclose other behavioral traits like rate limits, authentication needs, or data freshness, which could be relevant for an API tool.

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

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

The description is a single, efficient sentence that front-loads the core purpose and lists the included data types without unnecessary words. Every part earns its place by clarifying what 'detailed MLB game information' entails, 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 low complexity (1 required parameter), rich annotations (covering safety and idempotency), and 100% schema coverage, the description is mostly complete. It specifies the data returned, which compensates for the lack of an output schema. However, it could improve by mentioning sibling tools or usage context to fully guide the agent in a server with many sports data 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?

Schema description coverage is 100%, with the single parameter 'gameId' fully documented in the schema as 'MLB game ID (SDIO numeric ID or ESPN event ID from the scoreboard).' The description doesn't add any parameter-specific details beyond this, such as format examples or where to find IDs. Baseline 3 is appropriate since the schema handles the parameter documentation adequately.

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 MLB game information including box score, play-by-play summary, and team stats.' It specifies the verb ('Get'), resource ('MLB game information'), and scope of data returned. However, it doesn't explicitly differentiate from sibling tools like 'bsi_get_mlb_scoreboard' or 'bsi_get_mlb_standings' which might provide overlapping or related information.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a game ID), compare it to siblings like 'bsi_get_mlb_scoreboard' for broader data or 'bsi_get_mlb_team' for team-specific info, or specify use cases (e.g., post-game analysis vs. live updates). This leaves the agent to infer usage from context 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?

Annotations cover read-only, open-world, idempotent, and non-destructive hints, so the agent knows this is a safe, repeatable query. The description adds valuable context beyond annotations: it specifies the data source (ESPN) and hints at the return format ('top players with supporting stats'), which helps set expectations. No contradiction with 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 brief and front-loaded with the core purpose. Both sentences add value: the first states the action and return, the second specifies the data source. No wasted words, though it could be slightly more 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 annotations cover safety and behavior well, and the schema fully describes parameters, the description is adequate but has gaps. It lacks output details (no schema provided), doesn't explain how many 'top players' are returned or any pagination, and offers no usage guidelines. For a read-only query tool, it's minimally viable but incomplete.

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 (stat and category). The description doesn't add any parameter-specific details beyond what's in the schema, such as examples beyond those listed or default behaviors. Baseline 3 is appropriate since the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: 'Get MLB statistical leaders by category' with specific resource (MLB leaders) and verb (get). It distinguishes from some siblings like 'bsi_get_mlb_game' or 'bsi_get_mlb_team' by focusing on leaders, though it doesn't explicitly differentiate from 'bsi_get_nba_leaders' or 'bsi_get_nfl_leaders' which have similar patterns for other sports.

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 guidance on when to use this tool versus alternatives is provided. The description doesn't mention when to choose this over other MLB tools (like standings or team stats) or other leader tools for different sports. It only states what it does, not when it's appropriate.

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 read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations: it specifies the data sources (SportsDataIO primary with ESPN fallback) and details the return content (team names, scores, venue, game status), which helps the agent understand data reliability and output 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 two sentences with zero waste: the first states purpose and return details, the second adds data source context. It's front-loaded with core functionality and appropriately sized for a simple tool.

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

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

For a simple read-only tool with one optional parameter and no output schema, the description is largely complete. It covers purpose, usage, return content, and data sources. The main gap is lack of explicit output structure details, but annotations provide safety context, making this adequate.

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

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

Schema description coverage is 100%, so the schema fully documents the single 'date' parameter. The description mentions 'for a given date' but doesn't add syntax or format details beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the specific action ('Get MLB scores and game results'), resource ('MLB'), and scope ('for a given date'). It distinguishes from siblings like 'bsi_get_mlb_game' (specific game) and 'bsi_get_mlb_standings' (standings data) by focusing on scoreboard results for a date.

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

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

The description provides clear context about when to use this tool ('for a given date') and what it returns ('live and final games'). It doesn't explicitly mention when not to use it or name alternatives among siblings, but the MLB-specific focus and date parameter naturally differentiate it from other sports 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?

Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true. The description adds valuable context about data sources (SportsDataIO primary with ESPN fallback), which helps the agent understand reliability and potential fallback behavior. No contradiction with annotations exists.

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 concise sentences with zero waste: the first states purpose and data elements, the second provides data source information. Every word adds value, and the structure is front-loaded with the core functionality.

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

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

For a zero-parameter read-only tool with comprehensive annotations, the description is mostly complete. It explains what data is returned and data sources. The main gap is lack of output schema, so the agent doesn't know the exact return format, but the description provides enough context for basic usage.

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 doesn't discuss parameters since none exist, focusing instead on what data is returned and the data sources.

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 current MLB standings') and resource ('MLB'), including the data elements returned (wins, losses, win percentage, division records). It distinguishes from siblings by specifying the sport (MLB) and type of data (standings), differentiating it from other MLB tools like bsi_get_mlb_game or bsi_get_mlb_team.

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 specifying 'current MLB standings' and mentioning data sources, but doesn't explicitly state when to use this tool versus alternatives like bsi_get_standings (generic) or bsi_get_mlb_scoreboard. No explicit when-not-to-use guidance or comparison with sibling tools is provided.

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

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

Annotations already indicate this is a read-only, non-destructive, idempotent, and open-world operation. The description adds no behavioral traits beyond this, such as rate limits, authentication needs, or error handling. It doesn't contradict annotations, but provides minimal additional context, meeting the lower bar set by comprehensive 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 a single, efficient sentence that front-loads the key information (action, resource, scope) with no wasted words. Every part earns its place by clarifying the tool's purpose and parameters succinctly.

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, 100% schema coverage, no output schema) and rich annotations, the description is reasonably complete. It specifies what data is returned ('record, venue, roster'), though without an output schema, more detail on return values could be helpful. The annotations cover safety and behavior well, making the description 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?

Schema description coverage is 100%, with the parameter 'teamId' fully documented in the schema (including examples like '24' for St. Louis Cardinals). The description only repeats that it uses 'ESPN team ID' without adding new semantic details, so it meets the baseline of 3 where 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 action ('Get'), resource ('MLB team detail'), and specific scope ('by ESPN team ID'), with explicit mention of included data fields ('record, venue, roster'). It distinguishes from sibling tools like 'bsi_get_mlb_game' or 'bsi_get_mlb_standings' by focusing on team details rather than games, standings, or other sports.

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 specifies when to use this tool ('by ESPN team ID'), implying it's for retrieving detailed information about a specific MLB team. However, it doesn't explicitly state when not to use it or name alternatives (e.g., 'bsi_get_mlb_standings' for standings instead of team details), though the context is clear enough for basic differentiation.

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, and non-destructive. The description adds value by specifying the types of information returned (box score, quarter scores, team stats), which isn't in the annotations. However, it doesn't disclose additional context like rate limits, authentication needs, or data freshness, leaving some 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 a single, efficient sentence that front-loads the core purpose ('Get detailed NBA game information') and lists specific data inclusions. There's no wasted verbiage or redundancy, making it highly concise and well-structured for quick understanding.

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

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

Given the tool's simplicity (one required parameter, rich annotations, no output schema), the description is adequate but not fully complete. It covers what data is returned but lacks details on output format, error handling, or examples. With annotations providing safety and idempotency, it's minimally viable but could benefit from more context for optimal agent use.

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

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

Schema description coverage is 100%, with the single parameter 'gameId' well-documented in the schema as 'NBA game ID (ESPN event ID from the scoreboard).' The description doesn't add any parameter-specific details beyond this, so it meets the baseline of 3 where the schema handles most of the work.

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 NBA game information including box score, quarter scores, and team stats.' It specifies the verb ('Get') and resource ('NBA game information') with concrete examples of data returned. However, it doesn't explicitly differentiate from sibling tools like 'bsi_get_nba_scoreboard' or 'bsi_get_nba_team', which might provide overlapping or related 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 sibling tools (e.g., 'bsi_get_nba_scoreboard' for summaries or 'bsi_get_nba_team' for team-specific data), prerequisites like needing a game ID, or contextual cues for selection. Usage is implied only by the name and description.

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: readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true. The description adds minimal context by specifying 'Data source: ESPN,' which provides useful information about the data origin. However, it doesn't disclose additional behavioral aspects like rate limits, authentication needs, or response format, leaving some gaps despite the 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 highly concise and well-structured in two sentences: the first states the tool's purpose with specific categories, and the second specifies the data source. Every word adds value without redundancy, 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 (0 parameters, no output schema) and rich annotations, the description is somewhat complete but has gaps. It covers the purpose and data source but lacks details on output format, data freshness, or error handling. With no output schema, the description should ideally hint at return values or structure, which it doesn't, making it minimally adequate but not fully comprehensive.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose and data source. This aligns with the baseline expectation for tools without parameters, as the schema fully handles the input structure.

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 NBA statistical leaders across points, rebounds, assists, and efficiency categories.' It specifies the verb ('Get'), resource ('NBA statistical leaders'), and scope ('points, rebounds, assists, and efficiency categories'), but doesn't explicitly differentiate from sibling tools like 'bsi_get_nfl_leaders' or 'bsi_get_mlb_leaders' beyond mentioning 'NBA' and 'ESPN' as 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools (e.g., 'bsi_get_nfl_leaders' for NFL data or 'bsi_get_leaderboard' for general leaderboards), prerequisites, or specific contexts. The only implicit usage cue is the mention of 'NBA' and 'ESPN,' which might hint at sports data retrieval but lacks explicit alternatives 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?

The description adds valuable behavioral context beyond annotations by specifying the data sources (ESPN primary with SportsDataIO fallback) and the types of games returned (live, upcoming, final). While annotations cover safety and idempotency, the description provides operational details about data provenance and coverage that aren't captured in structured fields.

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

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

The description is perfectly concise with two sentences that each earn their place: first sentence states purpose and scope, second sentence adds valuable behavioral context about data sources. No wasted words, front-loaded with core functionality.

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

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

Given the tool's moderate complexity (single optional parameter), rich annotations covering safety and idempotency, but no output schema, the description provides good coverage. It explains what data is returned (team names, scores, venue, game status) and data sources, though doesn't detail response format or pagination. For a read-only query tool with good annotations, this is reasonably complete.

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

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

With 100% schema description coverage, the input schema already fully documents the single parameter. The description mentions 'for a given date' which aligns with the schema but doesn't add additional semantic context beyond what's already in the structured field. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the verb 'Get' and resource 'NBA scores and game results' with specific scope 'for a given date'. It distinguishes from siblings by specifying NBA content, unlike other sports tools (CFB, MLB, NFL) or generic scoreboard 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 provides clear context for when to use this tool (NBA scores on a specific date) but doesn't explicitly mention when not to use it or name specific alternatives. The sibling tools list shows clear alternatives for other sports, but the description doesn't guide the agent on choosing between NBA-specific tools like bsi_get_nba_game.

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 provide read-only, open-world, idempotent, and non-destructive hints, covering safety and behavior. The description adds context about what data is returned (wins, losses, etc.) and the conference grouping, which is useful but doesn't disclose additional traits like rate limits, data freshness, or error conditions. No contradiction with 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 a single, efficient sentence that front-loads the core purpose ('Get current NBA standings') and adds necessary details without waste. Every word contributes to clarity, 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 (0 parameters, no output schema) and rich annotations, the description is complete enough for a read-only data retrieval tool. It specifies the sport, data type, and key fields, though it could slightly improve by mentioning if it returns all teams or just top ones, but this is minor.

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, and the baseline for 0 parameters is 4, as it doesn't need to compensate for any gaps.

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 current NBA standings') and resource ('NBA standings'), including the scope ('by conference') and key data points ('wins, losses, win percentage, and games back'). It distinguishes from siblings like 'bsi_get_standings' by specifying NBA context and from other NBA tools by focusing on standings rather than games or teams.

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

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

The description implies usage context through 'current NBA standings by conference,' suggesting it's for real-time conference-level standings. However, it doesn't explicitly state when to use this tool versus alternatives like 'bsi_get_nba_scoreboard' or 'bsi_get_nba_team,' nor does it mention any 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 indicate this is a read-only, non-destructive, idempotent operation with open-world semantics. The description adds value by specifying the scope of returned data (record, venue, roster), which helps the agent understand what to expect beyond just the team ID. No contradictions with annotations exist.

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

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

The description is a single, efficient sentence that front-loads the core purpose and includes all necessary information without redundancy. Every word contributes to understanding the tool's function, 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 simple parameter schema (1 required parameter with full coverage) and rich annotations (readOnly, openWorld, idempotent, non-destructive), the description provides adequate context by specifying the data returned. However, without an output schema, it could benefit from more detail on the response format or potential limitations, though the annotations help mitigate this gap.

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, clearly documenting the single required parameter (teamId) with examples. The description reinforces this by mentioning 'ESPN team ID' but doesn't add significant semantic details beyond what the schema provides, such as format constraints or additional context about the ID source.

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 ('NBA team detail') with specific data elements (record, venue, roster) and identifies the key parameter (ESPN team ID). It distinguishes from siblings by focusing on NBA teams, but doesn't explicitly contrast with similar tools like bsi_get_nba_standings or bsi_get_team_schedule.

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 specifying 'by ESPN team ID,' suggesting this tool is for retrieving detailed information when you have that identifier. However, it doesn't provide explicit guidance on when to use this versus alternatives like bsi_get_nba_standings for broader team data or bsi_get_player_stats for roster details, nor does it mention prerequisites or exclusions.

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

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

Annotations already provide strong behavioral hints (readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false), so the description's burden is lower. It adds context about the type of information returned (box score, scoring summary, team stats), which is useful beyond annotations. However, it doesn't disclose rate limits, authentication needs, or specific behavioral traits like response format or error handling.

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 ('Get detailed NFL game information') and specifies included data types. There is zero waste—every word contributes to understanding the tool's function 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 low complexity (1 parameter, no output schema) and rich annotations covering safety and behavior, the description is reasonably complete. It specifies the information returned, which compensates for the lack of output schema. However, it could be more complete by mentioning data sources (ESPN) or typical use cases, though annotations reduce this need.

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 'gameId' fully documented in the schema as 'NFL game ID (ESPN event ID from the scoreboard).' The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without compensating value.

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 NFL game information including box score, scoring summary, and team stats.' It specifies the verb ('Get'), resource ('NFL game information'), and scope ('detailed'), distinguishing it from siblings like scoreboard or standings tools. However, it doesn't explicitly differentiate from other game-specific tools (e.g., bsi_get_nba_game) beyond the NFL context.

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 when to choose this over bsi_get_nfl_scoreboard for game details, bsi_get_nfl_team for team stats, or other game-specific tools for different sports. Usage is implied by the name and description but not explicitly stated.

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 provide readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds the data source ('ESPN'), which is useful context not in annotations. However, it doesn't disclose behavioral traits like rate limits, response format, or pagination, leaving gaps despite 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 a single, efficient sentence that front-loads the core purpose and adds the data source as supplementary information. There's no wasted text, repetition, or unnecessary details, 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 simplicity (0 parameters, no output schema) and rich annotations, the description is adequate but incomplete. It covers the what and data source but lacks details on output format, usage context, or behavioral constraints. Annotations help, but the description should provide more guidance for effective use.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing on the tool's purpose. A baseline of 4 is applied since no parameters exist, and the description doesn't attempt to explain non-existent inputs.

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

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

The description clearly states the tool's purpose: 'Get NFL statistical leaders across passing, rushing, receiving, and defensive categories.' It specifies the verb ('Get'), resource ('NFL statistical leaders'), and scope (multiple categories). However, it doesn't explicitly differentiate from sibling tools like 'bsi_get_mlb_leaders' or 'bsi_get_nba_leaders' beyond mentioning 'NFL' and 'ESPN' as 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools, specify use cases, or indicate prerequisites. The only contextual hint is the data source ('ESPN'), which doesn't help in tool selection among similar sports data 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?

Annotations cover read-only, open-world, idempotent, and non-destructive traits. The description adds value by specifying data sources (ESPN primary with SportsDataIO fallback) and return content (live, upcoming, final games with team names, scores, venue, status), which are not in 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, front-loaded with core purpose, zero waste. First sentence covers what, for whom, and returns; second adds data source context efficiently.

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

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

For a simple read-only tool with good annotations and no output schema, the description is mostly complete—covers purpose, data sources, and return content. Could improve by hinting at output structure or error cases, but adequate given low complexity.

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

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

Schema description coverage is 100% (parameter 'date' fully described in schema). The description mentions 'for a given date' but adds no extra meaning beyond schema. Baseline 3 is appropriate as 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 verb ('Get') and resource ('NFL scores and game results'), specifies the scope ('for a given date'), and distinguishes from siblings by focusing on NFL specifically (vs. CFB, MLB, NBA, or general scoreboards in the sibling list).

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 provides clear context ('for a given date') and implies usage for NFL score data, but does not explicitly state when NOT to use it or name alternatives (e.g., bsi_get_nfl_game for single games, bsi_get_scoreboard for other sports).

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 provide strong behavioral hints (read-only, open-world, idempotent, non-destructive), so the bar is lower. The description adds value by specifying the scope ('current') and data structure ('by division and conference'), which aren't covered by annotations. It doesn't contradict annotations, as 'Get' aligns with read-only, and no additional behavioral traits like rate limits are disclosed.

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 all essential information: action, resource, organizational method, and included data. There's no redundancy or wasted words, making it highly concise and front-loaded with key details.

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

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

Given the tool's low complexity (0 parameters, no output schema) and rich annotations, the description is mostly complete. It clearly states what the tool does and what data to expect. However, it doesn't specify the return format (e.g., JSON structure) or any limitations (e.g., data freshness), which could be helpful despite the annotations covering safety aspects.

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, focusing instead on the tool's purpose and output. This meets the baseline of 4 for zero parameters, as it avoids unnecessary details.

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

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

The description clearly states the verb ('Get') and resource ('current NFL standings'), specifies the organizational structure ('by division and conference'), and lists the key data points included ('wins, losses, ties, and win percentage'). It effectively distinguishes this tool from sibling tools like 'bsi_get_cfb_standings' or 'bsi_get_mlb_standings' by explicitly mentioning 'NFL'.

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 specifying 'current NFL standings,' suggesting it's for retrieving up-to-date league standings. However, it doesn't explicitly state when to use this tool versus alternatives (e.g., 'bsi_get_standings' without sport specification) or provide any exclusions or prerequisites, leaving some ambiguity.

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 provide readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, indicating a safe, read-only operation. The description adds value by specifying the type of details returned (record, venue, roster), which is not covered by annotations. It does not contradict annotations, as 'Get' 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 a single, efficient sentence that front-loads the purpose and key details without unnecessary words. Every part of the sentence contributes to understanding the tool's function, making it well-structured and concise.

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

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

Given the tool's low complexity (one required parameter), rich annotations (covering safety and behavior), and no output schema, the description is mostly complete. It specifies what details are returned, but could benefit from mentioning the data source (ESPN) or format of the response, though annotations help fill some gaps.

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

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

The input schema has 100% description coverage, with the 'teamId' parameter fully documented in the schema. The description does not add any parameter details beyond what the schema provides, such as additional examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'Get' and the resource 'NFL team detail', specifying the scope with '(record, venue, roster)' and the required identifier 'by ESPN team ID'. It distinguishes from sibling tools like 'bsi_get_nfl_game' or 'bsi_get_nfl_standings' by focusing on team details rather than games, standings, or other sports.

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 NFL team details are needed, but it does not explicitly state when to use this tool versus alternatives (e.g., 'bsi_get_nfl_standings' for standings or 'bsi_get_cfb_team' for college football teams). No exclusions or prerequisites are mentioned, leaving usage context inferred rather than clearly defined.

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.

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

The description adds valuable behavioral context beyond annotations: it specifies the tool searches the open web with default trusted domains and mentions provider fallback (Exa with Tavily). While annotations cover safety (readOnly, non-destructive, idempotent), the description provides operational details about search scope and infrastructure that aren't captured in 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 efficiently structured with three sentences that each serve distinct purposes: stating the search scope, providing usage guidelines, and detailing operational defaults. There's no wasted text, and key information is front-loaded appropriately.

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, comprehensive annotations, and full schema coverage, the description provides strong contextual information about purpose, usage, and operational behavior. The main gap is the lack of output schema, but the description compensates well by clearly defining the tool's scope and relationship to sibling 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?

With 100% schema description coverage, the input schema already thoroughly documents all three parameters. The description doesn't add parameter-specific information beyond what's in the schema, so it meets the baseline expectation without providing additional 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 tool searches the open web for specific college-baseball content types (news, scouting reports, etc.) and distinguishes it from sibling stats tools by specifying it provides narrative context rather than numbers. It uses specific verbs ('search') and resources ('college-baseball news, scouting reports, etc.') with explicit differentiation from siblings.

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

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

The description explicitly states when to use this tool ('when a question needs narrative context, not just numbers') and references sibling stats tools as alternatives. It also provides context about default trusted domains and fallback providers, offering clear guidance on appropriate usage scenarios.

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