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

No annotations are provided, so the description carries the full burden. It discloses the return format ('YYYY-MM-DD format') and that it returns a string, which adds useful behavioral context. However, it does not mention potential issues like timezone handling, freshness of data, or error conditions, leaving some behavioral aspects unspecified.

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

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

The description is extremely concise and well-structured, with two sentences that efficiently convey the purpose and return format without any wasted words. It is front-loaded with the main action and follows with necessary details, making it easy to 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 annotations, but has an output schema), the description is complete enough. It explains what the tool does and the return format, and since an output schema exists, it does not need to detail return values further. The description adequately covers all necessary context for this straightforward tool.

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

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

The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description does not add parameter semantics beyond the schema, but since there are no parameters, this is acceptable. A baseline of 4 is appropriate as the description compensates by being complete for a parameterless tool.

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

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

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('the current date'), and it distinguishes itself from sibling tools like 'get_current_time' by focusing specifically on date rather than time. The description is precise and unambiguous about what the tool does.

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 that it returns the current date, which suggests it should be used when the current date is needed. However, it does not explicitly state when to use this tool versus alternatives like 'get_current_time' or provide any exclusions. The context is clear but lacks explicit guidance on tool selection.

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

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

No annotations are provided, so the description carries the full burden. It discloses the return format ('HH:MM:SS format'), which is useful behavioral context. However, it doesn't mention other traits like whether it's timezone-aware, real-time vs. cached, or any potential errors, leaving some gaps in transparency.

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 extremely concise and well-structured: a single sentence stating the purpose, followed by a clear 'Returns' section with format details. Every sentence earns its place with no wasted words, making it easy 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 (0 parameters, simple output), the description is mostly complete. It explains what the tool does and the return format. Since an output schema exists, it doesn't need to detail return values further. A slight deduction because it could mention timezone behavior or edge cases 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?

The tool has 0 parameters, and schema description coverage is 100%, so the baseline is 4. The description appropriately doesn't discuss parameters, focusing instead on the return value, which aligns with 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 tool's purpose with a specific verb ('Get') and resource ('the current time'), making it immediately understandable. However, it doesn't differentiate from its sibling 'get_current_date' beyond the obvious time vs. date distinction, which is why it doesn't reach 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 no guidance on when to use this tool versus alternatives. While the tool name and purpose are self-explanatory, there's no explicit mention of when to choose this over 'get_current_date' or other time-related tools that might exist in a broader 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?

No annotations are provided, so the description carries the full burden. It states it's a read operation ('Get'), but lacks details on permissions, rate limits, error handling, or data format beyond 'dict.' For a tool with no annotations, this is insufficient behavioral disclosure.

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

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

The description is appropriately sized and front-loaded with the main purpose. The 'Args' and 'Returns' sections are structured but could be more integrated. No wasted sentences, though it could be slightly more polished.

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

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

Given 1 parameter, 0% schema coverage, no annotations, and an output schema (implied by 'Returns: dict'), the description is minimally adequate. It covers the basic purpose and parameter, but lacks context on usage, behavioral traits, or deeper semantics, making it incomplete 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 0%, so the description must compensate. It adds minimal semantics: 'award_id (int): Award ID.' This clarifies the parameter type and purpose slightly beyond the schema's 'type: integer,' but doesn't explain valid ranges, examples, or how to obtain award IDs. Baseline is 3 due to low coverage and partial compensation.

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 award recipients for a specific award.' It specifies the verb ('Get') and resource ('award recipients'), distinguishing it from siblings like get_mlb_players or get_mlb_teams. However, it doesn't explicitly differentiate from other award-related tools (none in siblings), so it's not a perfect 5.

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

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

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context, or exclusions, such as whether it's for current or historical awards, or if there are other tools for award lists. It's a basic statement without 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?

No annotations are provided, so the description carries full burden. It states it 'returns dict: Boxscore information' which gives some behavioral context about the return type, but doesn't disclose important traits like whether this is a read-only operation, potential rate limits, authentication needs, or what happens with invalid game_ids. For a tool with no annotations, this leaves significant gaps.

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

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

The description is well-structured with clear sections (Args, Returns) and front-loads the core purpose. The 'Args' and 'Returns' sections are helpful but could be more integrated. Every sentence serves a purpose, though the parameter explanations could be slightly more detailed given the schema coverage gap.

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

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

Given the tool has an output schema (which handles return values), 3 parameters with 0% schema coverage, and no annotations, the description does reasonably well. It explains all parameters and the return type, though it could better address behavioral aspects like error conditions or data freshness given the timecode parameter.

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

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

With 0% schema description coverage, the description adds meaningful context for all 3 parameters: game_id's purpose, timecode as a 'snapshot' indicator, and fields as a comma-separated list. However, it doesn't provide format details (e.g., timecode format, available field options) or examples that would fully compensate for the schema coverage gap.

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 'boxscore for a specific game by game_id', making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like get_mlb_linescore or get_mlb_game_scoring_plays, which also provide game-related data but for different aspects.

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 doesn't mention when this tool is appropriate compared to sibling tools like get_mlb_linescore (which might provide summary scoring) or get_mlb_game_scoring_plays (which might provide play-by-play 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Get[s] draft information' and returns a 'dict,' implying a read-only operation with dictionary output, but it doesn't disclose critical traits such as authentication needs, rate limits, error handling, data freshness, or what specific draft information is included (e.g., picks, teams, players). This leaves significant gaps for an agent to understand the tool's 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 appropriately sized and front-loaded, with the main purpose stated first ('Get draft information for a specific year.') followed by brief Arg and Return sections. Every sentence adds value, though the structure is basic and could be more streamlined (e.g., integrating Args/Returns into a single sentence). It avoids redundancy and waste, earning a high score for efficiency.

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 nested objects) and the presence of an output schema (implied by 'Returns: dict'), the description is somewhat complete but has gaps. It covers the basic purpose and parameter semantics but lacks behavioral context (e.g., no annotations, no details on output structure beyond 'dict') and usage guidelines. This makes it adequate for a simple tool but not fully informative.

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

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

The input schema has 0% description coverage, with one parameter 'year_id' of type 'integer' but no further details. The description adds minimal semantics by specifying 'year_id (int): Draft year,' which clarifies the parameter's purpose as the draft year. However, it doesn't compensate fully for the schema gap—e.g., no info on valid year ranges or format—so it meets the baseline for low schema coverage without fully addressing the deficiency.

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 as 'Get draft information for a specific year,' which includes a specific verb ('Get') and resource ('draft information') with a scope ('for a specific year'). It distinguishes from most siblings (e.g., get_mlb_awards, get_mlb_schedule) by focusing on draft data, though it doesn't explicitly differentiate from non-draft tools like get_mlb_players or get_mlb_teams, which prevents 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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., valid year ranges), exclusions, or comparisons to sibling tools like get_mlb_players or get_mlb_teams that might overlap in data retrieval. Usage is implied only by the tool's name and purpose, lacking explicit 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?

No annotations are provided, so the description carries the full burden. It states the tool retrieves highlights but does not disclose behavioral traits such as rate limits, authentication needs, data format (e.g., video URLs, text summaries), or error handling. The description is minimal and lacks essential context for safe and effective use.

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

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

The description is appropriately sized and front-loaded: the first sentence clearly states the purpose, followed by structured Args and Returns sections. Every sentence earns its place without redundancy, making it efficient and easy to parse.

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

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

Given the tool's moderate complexity (single parameter, no annotations, but with an output schema), the description is partially complete. It covers the basic purpose and parameter but lacks details on usage context, behavioral traits, and parameter semantics. The output schema exists, so the description need not explain return values, but overall gaps remain.

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

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

The description adds minimal meaning beyond the input schema. It specifies that game_id is an integer for identifying a game, which aligns with the schema. However, with 0% schema description coverage, the description does not compensate by explaining where to find game_id values or their format (e.g., MLB game IDs). The baseline is 3 due to the single parameter being straightforward.

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 game highlights for a specific game by game_id.' It specifies the verb ('Get'), resource ('game highlights'), and key identifier ('game_id'). However, it does not explicitly differentiate from siblings like get_mlb_game_scoring_plays or get_mlb_boxscore, which might provide overlapping or related game 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 mentions 'game highlights' but does not clarify what constitutes highlights (e.g., video clips, key moments) or how it differs from other game-related tools like get_mlb_game_scoring_plays. No exclusions or prerequisites are 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it's a read operation ('Get'), but doesn't cover aspects like error handling (e.g., invalid game_id), rate limits, authentication needs, or data freshness. The description lacks behavioral context beyond the basic action, which is insufficient for a tool with no annotation coverage.

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

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

The description is appropriately sized and front-loaded, with the core purpose in the first sentence and additional details in a structured Args/Returns format. There's no wasted text, though the 'Args' and 'Returns' sections are somewhat redundant given the input/output schemas, but they add clarity in a concise manner.

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) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and 0% schema coverage, it should provide more behavioral context (e.g., error cases, data scope) to fully guide the agent, making it incomplete for optimal 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 0%, so the schema provides no parameter details. The description adds minimal semantics by specifying 'game_id (int): The game ID,' which clarifies the parameter's purpose and type. However, it doesn't explain format constraints (e.g., valid ranges, source) or examples, leaving gaps despite the single parameter.

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 as 'Get lineup information for a specific game by game_id,' specifying the verb ('Get'), resource ('lineup information'), and key identifier ('game_id'). It distinguishes from most siblings (e.g., get_mlb_boxscore, get_mlb_linescore) by focusing on lineups, though it doesn't explicitly contrast with get_mlb_roster or get_mlb_players, which are related but not identical.

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 valid game_id), exclusions, or comparisons to siblings like get_mlb_roster (team rosters) or get_mlb_players (player lists), leaving the agent to infer usage from the name 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves data ('Get'), implying a read-only operation, but does not specify if it requires authentication, has rate limits, or details about the return format beyond 'dict: Game pace statistics.' For a tool with no annotations, this leaves significant gaps in understanding its operational 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 appropriately sized and front-loaded, with the core purpose stated first, followed by structured sections for Args and Returns. It avoids unnecessary verbosity, though the Args section could be more integrated into the main text for better flow.

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 (2 parameters, no nested objects) and the presence of an output schema (implied by 'Returns: dict'), the description is somewhat complete but lacks depth. It covers basic purpose and parameters but misses behavioral context and usage guidelines, which are important 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?

The description adds minimal semantics: it explains that 'season' is a season year and 'sport_id' defaults to 1 for MLB. With 0% schema description coverage and 2 parameters, this provides some clarification beyond the bare schema. However, it does not fully compensate for the lack of schema descriptions, such as explaining what 'game pace statistics' entail or valid ranges for parameters.

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

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

The description clearly states the tool's purpose: 'Get game pace statistics for a given season.' It specifies the verb ('Get'), resource ('game pace statistics'), and scope ('for a given season'). However, it does not explicitly differentiate from sibling tools like 'get_mlb_schedule' or 'get_mlb_standings', which also retrieve season-related data, leaving some ambiguity about when to choose this specific tool.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions the default sport_id for MLB but does not explain why one would use this tool over other MLB-related siblings like 'get_mlb_schedule' or 'get_mlb_standings'. There is no context on prerequisites, exclusions, or comparative use cases.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a 'Get' operation, implying it's read-only, but doesn't clarify authentication needs, rate limits, error handling, or what 'plays' entails (e.g., format, pagination). For a tool with 4 parameters and no annotation coverage, this leaves significant gaps in understanding its 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 well-structured and appropriately sized. It starts with a clear purpose sentence, followed by an 'Args' section detailing parameters and a 'Returns' section. While efficient, the 'Args' formatting could be slightly more concise, but overall, it avoids waste and is 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 (4 parameters, no annotations, but with an output schema), the description is adequate but has gaps. It covers parameters well and mentions the return type, but lacks behavioral context like error cases or usage scenarios. The output schema likely handles return values, so the description doesn't need to detail them, but overall completeness is minimal viable.

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

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

The description adds substantial value beyond the input schema, which has 0% description coverage. It explains each parameter's purpose: game_id identifies the game, eventType filters plays (with examples like 'scoring_play'), timecode specifies a snapshot, and fields controls output inclusion. This compensates fully for the schema's lack of descriptions, making parameter usage clear.

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 plays for a specific game by game_id, with optional filtering by eventType.' This specifies the verb ('Get'), resource ('plays'), and scope ('specific game'), though it doesn't explicitly distinguish it from sibling tools like get_mlb_game_highlights or get_mlb_game_lineup, which prevents 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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like get_mlb_boxscore or get_mlb_linescore that might overlap in functionality, nor does it specify prerequisites or exclusions. The agent must infer usage from the purpose alone.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return type ('dict: Linescore information') but lacks details on error handling, rate limits, authentication needs, or what specific data the linescore includes. This is insufficient for a tool with no annotation coverage.

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

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

The description is front-loaded with the core purpose, followed by structured Args and Returns sections. It's efficient with minimal waste, though the Args section could be slightly more detailed given the lack of schema descriptions.

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 (retrieving sports data), no annotations, and an output schema present, the description is minimally adequate. It covers the basic purpose and parameter but lacks behavioral details and usage guidance, leaving gaps in completeness.

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

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

The description adds meaningful context for the single parameter by specifying that game_id is an integer used to identify a specific game, which clarifies its purpose beyond the schema's basic type definition. Since there's only one parameter and schema description coverage is 0%, this compensation is adequate.

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 linescore') and the target resource ('for a specific game by game_id'), making the purpose immediately understandable. It distinguishes from siblings like get_mlb_boxscore or get_mlb_game_scoring_plays by specifying it retrieves linescore information, though it doesn't explicitly contrast with them.

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 like get_mlb_boxscore or get_mlb_game_scoring_plays, nor does it mention prerequisites or context for usage. It simply states what it does without indicating appropriate scenarios.

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

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

No annotations are provided, so the description carries the full burden. It states the tool retrieves information, implying a read-only operation, but does not disclose behavioral traits such as authentication requirements, rate limits, error handling, or data freshness. The description lacks context beyond the basic purpose.

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

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

The description is appropriately sized and front-loaded, with the purpose stated first. The 'Args' and 'Returns' sections are structured but slightly redundant since an output schema exists. Every sentence adds value, though it could be more concise by omitting the return note.

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 parameter) and the presence of an output schema, the description is reasonably complete. It covers the purpose and parameter basics, and the output schema handles return values. However, it lacks behavioral context and usage guidelines, which are minor gaps in this simple case.

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

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

Schema description coverage is 0%, but the description adds meaning by specifying that 'player_id' is an integer and used to identify a specific player. However, it does not explain the format, range, or source of player IDs, leaving gaps in parameter understanding. With one parameter, the baseline is 4, but the lack of detailed semantics reduces the score.

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 information about a specific player by ID.' It specifies the verb ('Get') and resource ('player information'), but does not distinguish it from sibling tools like 'get_mlb_players' or 'get_mlb_search_players', which might retrieve player lists or search results instead of individual player details.

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

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

No guidance is provided on when to use this tool versus alternatives. The description does not mention sibling tools like 'get_mlb_players' (for lists) or 'get_mlb_search_players' (for searches), leaving the agent to infer usage based on 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool 'Get all players' but doesn't describe key behaviors: whether it's paginated, rate-limited, requires authentication, returns structured data, or handles errors. The description is minimal and lacks operational context, leaving significant gaps for a tool with parameters and output.

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

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

The description is well-structured and concise. It starts with a clear purpose statement, followed by 'Args' and 'Returns' sections that efficiently document parameters and output. Every sentence earns its place, with no redundant or vague language, making it easy 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 has an output schema (returns dict), the description doesn't need to detail return values. However, with no annotations and 2 parameters, the description is minimal—it covers basic parameter semantics but lacks behavioral context (e.g., pagination, error handling). For a simple read operation, this might be adequate, but it falls short of being fully informative for reliable 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 0%, so the description must compensate. It adds meaningful context: 'sport_id' is explained with a default value (1 for MLB) and 'season' is clarified as an optional filter by year. This goes beyond the schema's basic types and defaults, providing practical usage hints. However, it doesn't cover all potential semantics like valid sport_id ranges or season format.

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

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

The description clearly states the tool's purpose: 'Get all players for a specific sport.' It specifies the verb ('Get') and resource ('players'), and while it doesn't explicitly differentiate from siblings like 'get_mlb_roster' or 'get_mlb_player_info', the focus on 'all players' provides some distinction. However, it could be more specific about scope or data granularity compared to similar 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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_mlb_roster' (which might list players by team) or 'get_mlb_player_info' (which might provide detailed individual data), leaving the agent to guess based on names alone. The only implied usage is for retrieving player lists, but no context or exclusions are 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns a dict with team roster information, but lacks details on permissions, rate limits, error handling, or data freshness. For a read operation with 6 parameters, this is insufficient to inform the agent about operational constraints or expected behavior beyond basic output.

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

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

The description is well-structured and appropriately sized. It front-loads the purpose, then details args and returns in clear sections. Each sentence adds value, with no redundant information. It could be slightly more concise by integrating the purpose with parameter explanations, but overall it's efficient.

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 (6 parameters, no annotations, output schema exists), the description is moderately complete. It covers the purpose and parameter semantics adequately, and the output schema handles return values. However, it lacks behavioral context (e.g., error cases, performance) and usage guidelines, which are gaps for a tool with multiple optional filters and sibling alternatives.

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

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

Schema description coverage is 0%, so the description must compensate. It effectively adds meaning by explaining each parameter's purpose and format (e.g., team can be 'numeric string, full name, abbreviation, or location', date format 'YYYY-MM-DD', fields as 'comma-separated list'). This clarifies usage beyond the bare schema, though it doesn't cover all nuances like valid values for rosterType or hydrate.

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 team roster for a specific team (ID or name), with optional filters.' It specifies the verb ('Get'), resource ('team roster'), and scope ('specific team'), distinguishing it from siblings like get_mlb_team_info or get_mlb_teams. However, it doesn't explicitly differentiate from get_mlb_players, which might overlap in functionality.

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 mentions optional filters but doesn't specify scenarios where this tool is preferred over siblings like get_mlb_players or get_mlb_team_info, nor does it mention prerequisites or exclusions. This leaves the agent without contextual usage cues.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but lacks critical behavioral details: whether it's read-only or mutative, rate limits, authentication needs, error handling, or what happens with invalid inputs. The description doesn't contradict annotations (none exist), but provides minimal behavioral context.

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

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

The description is appropriately sized and front-loaded with the core purpose in the first sentence. The Args and Returns sections are structured clearly. While efficient, the 'Returns' section could be slightly more informative given the output schema exists.

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 annotations, but has output schema), the description is partially complete. It covers parameters well but lacks behavioral context and usage differentiation from siblings. The output schema reduces need to explain return values, but more guidance on when to use this specific tool would improve 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?

With 0% schema description coverage, the description compensates well by explaining all 4 parameters in the Args section. It clarifies that player_ids is 'comma-separated,' season is a 'year,' stat_name is 'optional' with examples, and group has two possible values. This adds significant meaning beyond the bare schema types.

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 sabermetric statistics (including WAR) for multiple players for a specific season.' It specifies the verb ('Get'), resource ('sabermetric statistics'), scope ('multiple players', 'specific season'), and distinguishes from siblings by focusing on sabermetrics rather than awards, boxscores, or other MLB 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 implies usage context by mentioning 'multiple players' and 'specific season,' but doesn't explicitly state when to use this tool versus alternatives like 'get_multiple_mlb_player_stats' or 'get_statcast_batter.' No exclusions or prerequisites are provided, leaving usage guidance incomplete.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions the tool returns schedule data, it doesn't describe important behavioral aspects like whether this is a read-only operation (implied but not stated), potential rate limits, authentication requirements, error conditions, or what format the schedule data takes beyond 'dict'. The description provides basic functionality but lacks operational context.

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

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

The description is well-structured with a clear purpose statement followed by organized sections for Args and Returns. Every sentence earns its place by providing essential information. It could be slightly more concise by integrating the parameter details more fluidly, but the structure is effective and information-dense without waste.

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

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

Given the tool's moderate complexity (4 parameters, filtering logic), no annotations, but with an output schema present, the description provides good coverage. The parameter semantics are thoroughly explained, and the presence of an output schema means the description doesn't need to detail return values. However, it lacks some behavioral context that would be helpful for a tool with filtering capabilities.

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

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

With 0% schema description coverage, the description fully compensates by providing detailed semantic information for all 4 parameters. It explains what each parameter represents (sport_id defaults to 1 for MLB, start_date/end_date format requirements, team can be ID or name with multiple formats), their requirements (start_date and end_date are required), and default behaviors. This adds significant value beyond the bare schema.

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

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

The description clearly states the specific action ('Get MLB schedule') and resources involved (date range, sport ID, team). It distinguishes itself from sibling tools like get_mlb_standings, get_mlb_teams, or get_mlb_boxscore by focusing specifically on schedule retrieval rather than standings, team lists, or game details.

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

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

The description provides clear context for when to use this tool (to get schedule data with specific filtering parameters). It doesn't explicitly mention when NOT to use it or name specific alternatives among the sibling tools, but the purpose is sufficiently distinct that the appropriate use case is evident from the description 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool searches by name and returns results, but doesn't cover critical aspects like whether it's read-only, potential rate limits, authentication needs, or error handling. For a search tool with zero annotation coverage, this is a significant gap.

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

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

The description is appropriately sized and front-loaded with the core purpose, followed by structured sections for Args and Returns. Every sentence earns its place with no wasted words, making it easy to scan and understand.

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

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

Given the tool's moderate complexity (3 parameters, 1 required), no annotations, and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the purpose and parameters well, but could improve by addressing behavioral aspects like search scope or limitations.

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

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

Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics by explaining that 'fullname' is the player name to search for, 'sport_id' defaults to 1 for MLB, and 'search_key' defaults to 'fullname.' This clarifies parameter purposes beyond the bare schema, though it doesn't detail possible values or constraints.

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

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

The description clearly states the tool's purpose as 'Search for players by name,' which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'get_mlb_players' or 'get_mlb_player_info,' which might also retrieve 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 no guidance on when to use this tool versus alternatives, such as 'get_mlb_players' (which might list all players) or 'get_mlb_player_info' (which might retrieve detailed info for a specific player). It lacks explicit when/when-not 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?

No annotations are provided, so the description carries the full burden. It mentions the tool searches and returns results, but lacks critical behavioral details: whether it's read-only (implied but not stated), how results are structured, if there are rate limits, authentication needs, or pagination. The description is minimal and doesn't compensate for missing 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 well-structured and front-loaded with the core purpose, followed by clear sections for Args and Returns. Every sentence earns its place: the first sentence defines the tool, and subsequent lines efficiently document parameters and return type without 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 no annotations, 0% schema coverage, but an output schema exists, the description is partially complete. It covers the basic purpose and parameters but lacks behavioral context (e.g., safety, limits) and doesn't explain return values since the output schema handles that. For a search tool with two parameters, this is minimally adequate but has clear 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?

Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining 'team_name' accepts 'name or ID' and 'search_key' can be 'name', 'id', or 'all', which clarifies beyond the bare schema. However, it doesn't detail format constraints (e.g., ID format) or default behavior for 'search_key', leaving gaps. Baseline 3 is appropriate as it adds some but not complete 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's purpose: 'Search for teams by name or ID.' This specifies the verb ('search'), resource ('teams'), and search criteria. It distinguishes from siblings like 'get_mlb_teams' (likely lists all teams) and 'get_mlb_team_info' (likely gets details for a specific team), though it doesn't explicitly contrast them.

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 prefer it over 'get_mlb_teams' (which might return all teams without filtering) or 'get_mlb_team_info' (which might require a specific team ID). Usage is implied only by 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?

With no annotations provided, the description carries full burden but only partially discloses behavioral traits. It mentions default values and parameter purposes, but doesn't cover important aspects like rate limits, authentication needs, error conditions, or pagination behavior. The description adds some context but leaves significant gaps.

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

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

The description is well-structured with a clear purpose statement followed by organized parameter documentation. While efficient, the parameter explanations could be slightly more concise. Every sentence serves a purpose, and the 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, 6 parameters with 0% schema coverage, and no annotations, the description does a good job explaining parameters and return values. However, with an output schema present, the return value explanation is somewhat redundant, and behavioral aspects like error handling remain undocumented.

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?

Given 0% schema description coverage, the description fully compensates by providing clear semantic explanations for all 6 parameters. Each parameter gets specific guidance on format, acceptable values, defaults, and purpose, adding substantial value beyond the bare schema.

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

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

The description clearly states the specific action ('Get current MLB standings') and resource ('for a given season'), distinguishing it from siblings like get_mlb_schedule or get_mlb_teams. It precisely identifies 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 context through parameter explanations (e.g., 'Defaults to current year'), but lacks explicit guidance on when to use this tool versus alternatives like get_mlb_teams or get_mlb_schedule. No when-not-to-use scenarios or sibling comparisons are 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns team information as a dict, which is helpful, but lacks critical details: it doesn't mention if this is a read-only operation (implied by 'Get' but not explicit), what permissions or authentication might be required, rate limits, error handling, or how the 'hydrate' and 'fields' parameters affect behavior. For a tool with 5 parameters and no annotations, this is insufficient.

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

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

The description is appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by structured sections for args and returns. Every sentence adds value, with no redundancy. It could be slightly more concise by integrating the args list into the main text, but it's efficient overall.

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

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

Given the complexity (5 parameters, no annotations, but an output schema exists), the description is partially complete. The output schema means the description doesn't need to detail return values, but it lacks behavioral context and full parameter guidance. It's adequate for basic use but has clear gaps for effective tool invocation, especially with 0% schema coverage and no 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 0%, so the schema provides no parameter details. The description adds some semantics: it explains 'team' accepts ID or name in various formats, and lists other parameters with brief hints (e.g., 'season year' for 'season'). However, it doesn't fully compensate for the coverage gap—e.g., it doesn't explain valid formats for 'hydrate' or 'fields', or what 'sport_id' refers to. With 5 parameters, this leaves significant 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 information about a specific team by ID or name.' It specifies the verb ('Get information') and resource ('a specific team'), and distinguishes it from siblings like 'get_mlb_teams' (which likely lists teams) and 'get_mlb_search_teams' (which likely searches). However, it doesn't explicitly differentiate from 'get_mlb_roster' or 'get_mlb_standings', which might also provide team-related data, so it's not a perfect 5.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to use 'get_mlb_team_info' over 'get_mlb_teams' (for listing teams) or 'get_mlb_search_teams' (for searching teams), nor does it specify prerequisites or exclusions. The agent must infer usage from the purpose alone.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool fetches data ('Get all teams') and mentions a default value for sport_id, but fails to disclose critical traits like whether it's read-only (implied but not stated), potential rate limits, authentication needs, error handling, or pagination behavior. For a data retrieval tool with zero annotation coverage, this is a significant gap.

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

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

The description is appropriately sized and front-loaded. The first sentence states the core purpose, followed by structured sections for Args and Returns. Every sentence adds value: the purpose, parameter explanations, and return type. There is no redundant or verbose content, 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 low complexity (2 parameters, no nested objects) and the presence of an output schema (which handles return values), the description is largely complete. It covers the purpose, parameters, and return type. However, it lacks behavioral context (e.g., safety, limits) and explicit usage guidelines, which are minor gaps in an otherwise adequate description for this simple tool.

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

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

The description adds meaningful semantics beyond the input schema. With 0% schema description coverage, the schema only defines types and defaults. The description explains that 'sport_id' defaults to 1 for MLB and 'season' filters teams by year, clarifying the purpose and usage of parameters that are otherwise undocumented. It compensates well for the low schema coverage, though it doesn't detail all possible values or constraints.

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

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

The description clearly states the tool's purpose: 'Get all teams for a specific sport.' It specifies the verb ('Get') and resource ('teams'), and distinguishes it from siblings like 'get_mlb_team_info' (which likely fetches details for a single team) and 'get_mlb_search_teams' (which likely involves search queries). However, it doesn't explicitly differentiate from all siblings, such as 'get_mlb_roster' (which might list players per team), keeping it from 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?

Usage guidelines are implied but not explicit. The description mentions filtering by sport and season, suggesting it's for retrieving team lists rather than detailed info or searches. However, it lacks clear when-to-use directives, such as advising to use 'get_mlb_team_info' for individual team details or 'get_mlb_search_teams' for filtered searches, 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Get[s] player stats' but doesn't mention whether this is a read-only operation, requires authentication, has rate limits, or what happens with invalid inputs. The description lacks behavioral traits beyond the basic action, leaving significant gaps for an agent.

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

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

The description is well-structured and appropriately sized. It starts with a clear purpose statement, followed by an 'Args' section listing parameters with brief semantics, and a 'Returns' section. There's minimal waste, though the 'Args' and 'Returns' labels add slight redundancy. Overall, it's efficient and front-loaded.

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

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

Given the tool's moderate complexity (5 parameters, 1 required) and no annotations, the description is partially complete. It covers the purpose and parameters but lacks behavioral context and usage guidelines. The presence of an output schema (implied by 'Returns: dict') reduces the need to explain return values, but overall, it's adequate with clear gaps for a statistical query 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 0%, so the description must compensate. It lists all 5 parameters with brief explanations (e.g., 'Stat group (e.g., hitting, pitching)'), adding meaning beyond the schema. However, it doesn't provide full details like valid values for 'group' or 'type', or how 'player_ids' are formatted, leaving some ambiguity. With 0% coverage, this is a moderate improvement but incomplete.

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 player stats by comma separated player_ids, group, type, season, and optional eventType.' It specifies the verb ('Get'), resource ('player stats'), and key parameters. However, it doesn't explicitly differentiate from sibling tools like 'get_mlb_player_info' or 'get_statcast_batter', 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 provides no guidance on when to use this tool versus alternatives. With many sibling tools for MLB data (e.g., 'get_mlb_player_info', 'get_statcast_batter'), there's no indication of how this tool differs in scope or when it's preferred. The description merely lists parameters without contextual usage advice.

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

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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: data source (MLB Statcast via pybaseball), potential error condition ('If the result is too large, returns an error message'), and return format. However, it doesn't mention rate limits, authentication needs, or what specific data fields to expect in the dictionary.

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?

Well-structured with clear sections (Parameters, Returns, Notes). The opening sentence efficiently states the purpose. Some redundancy exists (parameters listed in both description and schema), but overall it's appropriately sized with useful information in each section.

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 3 parameters with 0% schema coverage and no annotations, the description provides good coverage: purpose, parameters, return format, error condition, and data source. With an output schema present, it doesn't need to detail return values. The main gap is lack of behavioral constraints like rate limits or authentication requirements.

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

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

Schema description coverage is 0%, so the description must compensate. It provides clear semantic meaning for all 3 parameters: player_id as 'MLBAM ID of the batter', start_date/end_date as date strings in 'YYYY-MM-DD' format with 'Required' indication. This adds substantial value beyond the bare schema, though it doesn't explain format constraints beyond date format.

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

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

The description clearly states the specific action ('Retrieve MLB Statcast data'), resource ('for a single batter'), and scope ('over a date range'). It distinguishes from siblings like get_statcast_pitcher (different player type) and get_statcast_team (different aggregation level).

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

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

The description implies usage context through the parameters (batter-specific, date range) and distinguishes from siblings by specifying 'single batter' vs team/pitcher tools. However, it doesn't explicitly state when NOT to use this tool or name specific alternatives for different query types.

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

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: data source (MLB Statcast via pybaseball), potential error condition (returns error if result too large), and return type (dictionary). However, it doesn't mention 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?

Well-structured with clear sections (Parameters, Returns, Notes) and front-loaded purpose statement. The Notes section could be slightly more concise, but overall information density is high with minimal waste.

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

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

Given no annotations, 0% schema coverage, but presence of output schema, the description provides good context: clear purpose, parameter semantics, return behavior, and data source. Could improve by mentioning sibling tool relationships or more behavioral details, but covers essentials adequately.

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

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

The description adds substantial value beyond the input schema (0% coverage). It provides semantic meaning for all three parameters: clarifies player_id is an 'MLBAM ID', specifies date formats ('YYYY-MM-DD'), and marks both dates as 'Required'. This fully compensates for the schema's lack of descriptions.

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

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

The description clearly states the tool's purpose with specific verb ('Retrieve') and resource ('MLB Statcast data for a single pitcher over a date range'). It distinguishes itself from siblings like 'get_statcast_batter' (pitcher vs batter) and 'get_statcast_team' (single pitcher vs 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 through parameter requirements (pitcher ID and date range) but doesn't explicitly state when to use this tool versus alternatives like 'get_statcast_batter' or 'get_statcast_team'. No guidance on prerequisites or exclusions 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?

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it specifies the underlying function (pybaseball `statcast`), mentions that large results may return an error, provides a link to documentation, and clarifies the team-level aggregation scope. It doesn't mention rate limits, authentication needs, or data freshness, but covers the essential operation characteristics.

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

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

The description is appropriately front-loaded with the core purpose, but the extensive field list (over 100 items) makes it lengthy. While the field list is necessary given the schema coverage gap, it reduces overall conciseness. The structured sections (Parameters, Returns, Notes) help organization but create some 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 complexity (4 parameters, 0% schema coverage, no annotations) and presence of an output schema, the description is quite complete. It explains parameters thoroughly, documents return behavior and error cases, and provides implementation context. The main gap is lack of explicit sibling tool differentiation, but overall it provides sufficient context 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 schema description coverage is 0%, so the description must fully compensate. It provides detailed parameter explanations: team ID/name format guidance, date format requirements, and an extensive list of available fields with defaults. This adds substantial meaning beyond the bare schema, making parameter usage clear despite the schema's lack of descriptions.

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

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

The description clearly states the specific action ('Retrieve MLB Statcast data'), target resource ('for all players on a team'), and scope ('over a date range'). It distinguishes itself from sibling tools like get_statcast_batter and get_statcast_pitcher by specifying team-level aggregation rather than individual player focus.

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

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

The description implies usage context through the parameter explanations and notes section, suggesting this tool is for bulk Statcast data retrieval. However, it doesn't explicitly state when to use this tool versus alternatives like get_statcast_batter or get_statcast_pitcher, nor does it mention any prerequisites or constraints beyond date ranges.

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