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

어노테이션이 전혀 없어 설명이 행동 특성을 모두 감당해야 하는데, 읽기 전용 여부, 조회 범위(리그/기간), 반환 형태(승패 요약인지 경기 목록인지) 등 어떤 동작 정보도 제공하지 않는다.

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?

두 문장으로 간결하고 목적이 앞에 배치되어 있다. 낭비는 없으나 정보량이 적어 최상위는 아니다.

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?

단순 조회 도구이고 파라미터도 2개뿐이라 최소한의 설명으로 호출은 가능하다. 그러나 출력 스키마가 없음에도 반환 내용(승패 전적, 경기 목록 등)에 대한 언급이 없어 완전하다고 보기는 어렵다.

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?

스키마 설명 커버리지가 100%로 두 파라미터(team_a, team_b)의 의미와 한국어/영어 입력 가능 여부가 이미 스키마에 문서화되어 있다. 설명은 이에 추가적인 의미를 제공하지 않으므로 기준선 3이 적절하다.

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?

명확한 동사(조회)와 대상(두 팀의 역대 맞대결 기록)을 제시하여 무엇을 하는지 분명하다. 다만 형제 도구(예: get_recent_results, get_standings)와의 구분을 명시적으로 언급하지는 않아 5점은 아니다.

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?

'상대전적 분석에 활용'이라는 한 줄로 사용 맥락을 암시하지만, 언제 이 도구를 쓰고 언제 다른 도구(예: get_recent_results)를 써야 하는지에 대한 명시적 안내나 제외 조건이 없다.

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 behavioral burden. It discloses that this is a read-style live score lookup, that goals and match time are included, and that an API-Football key is required. However, it omits rate limits, quota behavior, data freshness, empty-live-match handling, and other operational details an agent might need.

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 concise and front-loaded: the core purpose comes first, followed by included data and an auth requirement. Every sentence is brief and relevant, though the parenthetical API key note and fragment-like phrasing keep it slightly short of ideal polish.

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 one-parameter tool with high schema coverage and no output schema, the description gives an adequate high-level view: live soccer scores, included data, and API key requirement. It does not describe return structure, empty-result behavior, or filtering nuances, but those gaps are moderate given the tool's simplicity.

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% for the single optional 'league' parameter, so the schema already carries parameter meaning. The description adds no additional parameter semantics, which matches the baseline of 3 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 states a specific verb and resource in Korean: retrieving real-time scores for currently in-progress soccer matches. It also names the included data, goals and match time, which makes the purpose clear. It does not explicitly distinguish itself from sibling tools like get_today_fixtures or get_recent_results, but the '현재 진행 중인' scope implies live matches rather than scheduled or completed ones.

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 by saying 'currently in-progress soccer matches,' but it never states when to use this tool instead of get_today_fixtures, get_recent_results, or other siblings. There are no explicit exclusions, prerequisites, or alternative selection guidance beyond the implied live-match 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?

With no annotations, the description carries full behavioral burden. It implies a read operation but doesn't disclose whether it requires network calls, rate limits, data freshness, or response format. For a stats retrieval tool, 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 a single concise sentence with no wasted words. It is front-loaded with the core purpose, though it lacks any additional structure or clauses for usage context.

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 tool with no annotations and no output schema, the description is too sparse. It doesn't clarify whether stats are averages per game, totals, or advanced metrics; it also omits error handling (e.g., unknown players) and data recency.

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

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

Schema coverage is 100%, so the parameter's meaning and format are fully documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, making the baseline 3 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 and resource: retrieving a player's average season stats. It distinguishes somewhat from siblings like get_live_scores or get_standings by focusing on individual player stats, though it doesn't explicitly name an alternative.

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 siblings such as get_live_scores or get_standings. There's no indication of prerequisites, context, or exclusions, leaving usage to inference.

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 behavioral burden. It does not disclose what '최근' (recent) means in terms of number of games, date window, or ordering, nor what happens for an unrecognized team name. The single mention of recency is too vague to be actionable.

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?

A single front-loaded sentence with no filler; the resource and scope come first and nothing is wasted. Appropriately sized for a one-parameter lookup 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 trivial single-param lookup with no output schema, the definition is roughly adequate, but the undefined recency window ('최근') is a genuine ambiguity that neither the schema nor the description resolves, and there is no guidance on missing teams or result volume.

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%, and the schema already documents team_name with concrete examples including both English and Korean forms. The description adds no syntax, aliasing, or validation detail beyond the schema, so the baseline 3 applies.

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 states a specific verb and resource ('특정 NBA 팀의 최근 경기 결과를 조회합니다' – retrieves a specific NBA team's recent game results), which is clear enough for an agent to understand the operation. However, it does not differentiate itself from the sibling 'get_recent_results', which appears to cover overlapping ground, leaving the agent to guess which to pick.

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?

There is no explicit when-to-use guidance, no mention of when not to use it, and no reference to alternatives such as get_recent_results or get_nba_today. The team-scoped nature is only implied by the parameter, not stated as a selection criterion.

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 implies a read-only lookup via '조회합니다' but does not clarify timezone scope, update frequency for in-progress games, permissions, or any other 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 a single front-loaded sentence with no wasted words. It is appropriately sized for the operation, though its brevity contributes to gaps elsewhere rather than being a conciseness problem.

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?

The tool is simple with zero parameters and no output schema, but the crowded sibling set makes disambiguation essential. The description does not explain how 'today's NBA game results and in-progress games' differs from get_live_scores or get_today_fixtures, leaving a significant contextual 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 tool has zero parameters, so the baseline is 4. The description adds no parameter meaning because there are no parameters to describe, which is appropriate for this dimension.

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 states a specific verb and resource: retrieving today's NBA game results and in-progress games. It is clear what the tool does, but it does not distinguish itself from overlapping siblings such as get_live_scores, get_today_fixtures, or get_recent_results.

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

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

There is no guidance on when to use this tool versus the many sibling tools covering live scores, today's fixtures, and recent results. The description only states what it returns, 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?

With no annotations, the description carries the full behavioral burden. The verb 조회 implies a read-only operation, but the description does not disclose freshness of results, rate limits, permissions, response format, or what 'recent' means exactly.

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?

A single front-loaded sentence with no filler or redundancy. It states the purpose immediately and every word earns its place.

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?

The description is adequate for a simple 2-parameter read tool with full schema coverage, but it omits any sport/league context that would help distinguish it from the NBA-specific siblings, and gives no indication of result freshness or return shape.

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 both team_name and count are already documented in the schema. The description adds only a vague team scope and 'recent' qualifier, not meaningful syntax or format details beyond the 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 states a specific verb (조회/retrieve), resource (경기 결과/match results), and scope (특정 팀/specific team), so the core action is clear. However, it does not differentiate from siblings like get_nba_team_games or get_live_scores, which could also return team-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?

No guidance is provided on when to choose this tool over alternatives such as get_live_scores, get_upcoming_fixtures, or get_nba_team_games. The purpose implies a use case, but there are no explicit conditions, 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?

No annotations are provided, so the description carries the full behavioral burden, yet it adds none: no note on pagination, rate limits, caching, or what a standings entry contains. '조회' implies a read, but nothing about the data source selection behavior ('auto' fallback) or freshness is 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?

A single efficient sentence with the resource front-loaded and zero filler. It is arguably too terse for what it omits, but nothing in it is wasted.

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

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

With no annotations and no output schema, the description is the only behavioral context available and it only restates the purpose. For a simple read tool with fully covered parameters this is minimally adequate, but the shape of the returned standings table is left entirely unexplained.

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 league list, season default, and source enum are already fully documented in the schema. The description adds no extra meaning about parameter interaction or valid combinations, so the baseline 3 applies.

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 states a specific verb and resource ('현재 순위표를 조회합니다' – retrieve the current standings table) scoped to a league. It is clearly distinct in intent from the fixture/score siblings, but it never names or contrasts with them, so sibling differentiation is left to inference.

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 is only implied: '현재' hints at current-season standings versus historical results, but there is no explicit when-to-use, when-not-to-use, or pointer to an alternative sibling. An agent can guess the context but gets no routing help.

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 behavioral burden. It does not disclose return format, pagination, error behavior, or freshness guarantees, and only briefly mentions that results may be included. This is insufficient for a read-only tool with no 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 two short, front-loaded sentences with zero waste. It immediately states the purpose and then adds a relevant behavioral note about results.

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 no-annotation, single-optional-parameter tool with no output schema, the description provides the core purpose and timezone but lacks sufficient behavioral detail (e.g., result inclusion criteria, update frequency). It 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 100%, and the schema already fully documents the single optional 'league' parameter with examples. The description adds no parameter details beyond what the schema provides, but baseline is 4 because schema coverage is high and the description implies filtering without contradicting it.

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 states a specific verb (조회합니다) and resource (오늘 예정된 축구 경기 일정), and notes the timezone basis (KST). It distinguishes itself from siblings like get_upcoming_fixtures and get_live_scores by scoping to 'today' and including results, though it doesn't explicitly contrast with those 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?

There is no explicit guidance on when to use this tool versus alternatives such as get_upcoming_fixtures or get_live_scores. Usage is only implied by the name and description; 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. It usefully discloses a non-obvious behavior (Korean team names auto-converted, e.g. 손흥민 → 토트넘), but says nothing about return format, data freshness, or rate limits.

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 tight sentences with the core action front-loaded and the notable Korean-name behavior appended 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?

With no annotations and no output schema, the description should do more: it never explains what a returned fixture contains or how count interacts with results. Adequate for selection, thin for correct 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?

Schema coverage is 100%, so the schema already documents both parameters. The description's Korean-name note and the example largely duplicate the schema's own description, adding only marginal value; baseline 3 applies.

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?

States a specific verb+resource: retrieves a specific team's upcoming fixture schedule. The word '다음 경기' (upcoming) implicitly separates it from get_recent_results and get_today_fixtures, but no sibling is named explicitly.

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 is implied (want the future schedule of one team) but there is no explicit when-to-use/when-not guidance or reference to alternatives like get_today_fixtures or get_live_scores, so the agent must infer boundaries.

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 behavioral burden. It only says what is shown, not how: no indication of read-only nature, no date boundary definition of 'this week', no mention of whether missing teams/leagues degrade gracefully, and no output shape even though no output schema exists. The lone behavioral hint is empty-result or unknown-team handling, which is absent.

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?

A single front-loaded sentence with no filler. It is compact and states the core value (aggregate fixtures + recent results at once), though the brevity is partly the cause of the missing scope and behavioral detail.

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 tool with no annotations, no output schema, and heavy sibling overlap, the description is too thin. It omits the week definition, NBA/league handling, behavior for unknown teams, and any return structure, so an agent cannot confidently decide when this beats the more specific 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%: the schema already documents teams (with an example) and include_nba (with its default). The description adds nothing beyond 'teams of interest' and implies a results/fixtures concept, so it sits at the baseline 3.

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 states a combined resource: this week's fixture schedule and recent results, aggregated per team. That is more specific than the name alone, but it is vague about scope versus siblings — get_today_fixtures, get_upcoming_fixtures, get_recent_results and get_nba_today all overlap with parts of this, and the description never carves out what this tool does differently (a weekly aggregated multi-team view).

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?

There is no when-to-use or when-not-to-use guidance. With nine sibling tools covering today, upcoming, live, recent, NBA-specific and per-team views, the agent is left to guess whether this is a convenience aggregation or a distinct data source. It does implicitly suggest 'weekly summary across several teams' but never contrasts with alternatives.

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