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

Annotations already declare readOnly=True, destructive=False, and idempotent=True, so the safety profile is clear. The description adds behavioral context by explaining the adjustment formula (avg_opponent_ppg / league_average_ppg) and how to interpret values relative to raw PPG. This goes beyond what annotations provide and helps the agent reason about the computation.

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 main purpose, followed by a compact formula explanation and an arguments line. Every sentence adds value, and the structure is clean. The 'Args' block is redundant with the schema but serves as a useful inline reminder given that the schema lacks 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?

The description explains what the tool returns (raw and adjusted PPG) and the reasoning behind the adjustment. With an output schema present, return values do not need to be spelled out. It does not cover error conditions or rate limits, but for a simple read-only tool this is adequate. The description is complete enough for an agent to invoke it correctly.

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 only defines team_id as a string with no description (0% coverage). The description compensates by stating 'team_id: ESPN numeric team ID', which clarifies the expected format and meaning. This is sufficient for the single parameter, though it could also mention that the team must exist in ESPN systems.

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 uses a specific verb+resource ('Get a team's raw points-per-game alongside an opponent-quality-adjusted PPG') and clearly distinguishes this from sibling tools like get_standings or get_strength_of_schedule. It names the exact metric and includes a concise definition.

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 the primary use case: retrieving a team's PPG with an adjustment for opponent quality. It does not explicitly discuss exclusions or alternatives, but the formula explanation gives context for when this metric is relevant. A clear alternative comparison is missing, so it does not fully reach a 5.

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

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint false). The description adds useful behavioral context by specifying the response includes score, venue, attendance, and a chronological list of key events. No contradiction with annotations.

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

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

The description is appropriately sized and front-loaded: purpose, return payload, usage, and parameter details in a compact structure. Every sentence earns its place with no filler or redundant restating of the schema.

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 one parameter, clear parameter semantics, and an output schema present, the description provides complete contextual coverage. It covers what the tool does, what it returns, and how to obtain the required ID, while annotations handle the safety profile.

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 0%, but the description fully compensates by explaining that match_id is an ESPN numeric event ID and providing a concrete example. It also tells the agent where to obtain the ID, which is exactly what is needed to populate the sole parameter correctly.

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 opens with 'Get detailed information for a single MLS match', which is a specific verb+resource statement. It immediately lists returned fields (score, venue, attendance, key events), clearly distinguishing it from sibling listing tools like get_scoreboard and get_team_schedule.

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

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

Explicitly instructs to use the match ID returned by get_scoreboard or get_team_schedule, giving clear context for when to invoke this tool. It does not state explicit exclusions, so it falls short of a 5, but the guidance is actionable and helpful.

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

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

Annotations already establish that this is a read-only, idempotent, non-destructive operation, so the description does not need to restate that. The description adds value by disclosing the return structure (headline, publication date, summary, link to ESPN) and the default limit, which goes beyond the schema. However, it does not mention any potential variability, such as the number of articles when 'recent' is ambiguous or possible downtime, but these are minor given the scope.

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 well-structured. It opens with a clear purpose statement, follows with the return details, and ends with a parameter explanation. Each sentence earns its place without redundant filler. The use of an 'Args:' section makes the parameter documentation easy to scan.

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 one optional parameter and an output schema, and the description covers the purpose, return fields, and parameter semantics. Annotations provide the safety and idempotency profile. No further details like authentication or rate limits are necessary for a read-only news-fetching tool, making the description complete for the tool's complexity.

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

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

Schema description coverage is 0%, so the description carries the full burden for explaining parameters. The description provides clear semantics: 'limit: Maximum number of articles to return (default 10).' This explains both the purpose of the parameter and its default value, which is not available in the schema beyond the default attribute. The explanation is precise and sufficient for a 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 verb and resource: 'Get recent MLS news articles.' This distinguishes it from sibling tools that focus on teams, scores, rosters, schedules, and standings, as news articles are a distinct resource. The specificity of 'recent MLS news' leaves no ambiguity about what the tool returns.

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 the tool should be used when a user wants recent MLS news, but it does not explicitly state when to use this tool versus alternatives or mention any exclusions. While the sibling tools are clearly different in topic, there is no direct guidance such as 'for scores, use get_scoreboard' or 'when you need news, use get_news.' The usage context is inferred rather than explicitly provided.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral detail: tiers are derived from live league standings, and tier_size has constraints (minimum 1, 2*tier_size <= league size). This goes beyond the annotations and helps the agent understand how the tool behaves at runtime.

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 well-structured. The first sentence states the purpose, followed by a brief explanation of tier derivation, a use-case rationale, and an Args section. Every sentence contributes unique information; there is no redundancy or verbosity.

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 only 2 parameters, an output schema, and clear annotations, the description fully covers the tool's behavior. It explains the tier logic, constraints, and provides a comparison to manual scanning, which together give an agent complete context for 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?

With 0% schema description coverage, the description must fully explain parameters. It does: team_id is identified as an ESPN numeric team ID, and tier_size is explained with its default value and validation constraints. This adds rich meaning beyond the bare schema property names.

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 opens with a specific verb and resource: 'Get a team's W-L-T splits against current top-tier, middle, and bottom-tier teams.' This clearly states what the tool does and uniquely distinguishes it from sibling tools like get_standings or get_team_schedule by focusing on opponent-tier splits. The purpose is precise and not 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 provides a clear contextual use case: 'Lets you ask "how does this team do against the top of the table?" without scanning every result manually.' This implies when to use the tool, but it does not explicitly name alternative tools or state when not to use it. Because it gives meaningful usage context without exclusions, it earns a 4.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent understands it's a safe read. The description adds the 'active roster' scoping detail and the source of team_id, which is useful behavioral context beyond the annotations.

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

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

The description is compact and front-loaded with the main purpose. It includes a minimal Args section with no redundant information; every sentence 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?

For a simple read-only tool with one parameter, clear annotations, and an output schema, the description covers purpose, usage, and parameter semantics sufficiently. It leaves no critical gaps for selection or 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 input schema provides no description for team_id, so the description must compensate. It clearly explains the parameter as an ESPN numeric team ID, gives a concrete example, and directs users to get_teams for the correct value—fully covering the parameter semantics.

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

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

The opening 'Get the active roster for an MLS team' is a clear verb+resource statement. It doesn't explicitly distinguish from sibling get_team, but 'roster' is specific enough and the mention of get_teams provides context.

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

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

The description explicitly instructs to use the team ID returned by get_teams, establishing a clear prerequisite and workflow. It doesn't discuss when not to use it, but the context implies this is the roster-specific tool.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, which cover the safety profile. The description adds meaningful behavioral context beyond annotations, such as the 'current matchweek' default behavior, the inclusive range semantics, and the requirement that end_date depends on date. No contradictions exist.

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

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

The description is concise and well-structured: a one-sentence summary followed by bullet-like argument behavior and an Args list. Every sentence adds value, with no repetition of schema details or fluff.

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

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

All argument behaviors and constraints are fully documented, including the default case, date-only case, and date-range case. An output schema exists, so return-value details are not required. The tool is simple and the description covers all necessary usage context.

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

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

Schema-description coverage is 0%, so the description carries the full burden. It explicitly defines each parameter, including the YYYYMMDD format and the dependency of end_date on date. This fully compensates for the absence of schema 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 opens with a specific verb and resource: 'Get MLS match scores and status for a date or date range.' It further clarifies behavior based on arguments (no args, date only, date+end_date), which clearly distinguishes it from siblings like get_match_details or get_team_schedule.

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

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

The description gives clear context for when to use each argument combination: no arguments returns the current matchweek, date returns a single day, and both date and end_date provide an inclusive range. It does not explicitly name alternative tools or exclusions, so it falls short of a 5.

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

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

Annotations already indicate read-only, idempotent, open world, and non-destructive. The description adds valuable behavioral detail beyond annotations: the response format (separate numbered tables per conference), ordering (points descending), and included stats (W/L/T, goals for/against, differential). This provides transparency about output structure without contradicting annotations.

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

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

The description is concise and front-loaded with the core purpose, followed by a brief, useful elaboration on the response format. Every sentence adds value; no wasted words.

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 no parameters, and the description fully explains what the output will contain. It provides enough context for an agent to select this tool and understand the response, even without the output schema being shown.

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 schema coverage is trivially 100%. The description doesn't need to explain parameters. Baseline for 0 params is 4.

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 function: 'Get the current MLS standings, grouped by conference.' It specifies the resource (MLS standings) and the organization (conference). It distinguishes from sibling tools like get_teams and get_scoreboard by focusing on standings.

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: use when you need current standings with conference breakdown. It doesn't explicitly name alternatives, but the context is clear enough given the sibling tools. A slightly higher score is not warranted because it doesn't say 'use instead of X'.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds behavioral context by specifying it computes average points-per-game of opponents in completed matches and returns a per-opponent breakdown, going beyond what annotations provide.

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 with the core purpose, followed by return details and a use case. It avoids fluff and includes only valuable information, with the Args section providing a clear parameter explanation.

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 single-parameter, read-only tool with a full output schema and rich annotations, the description is complete. It covers purpose, scope, parameter format, and suggests when to use it, leaving no significant 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 coverage is 0%, but the description fully compensates by explaining team_id as an 'ESPN numeric team ID' with a concrete example ('18418' for Atlanta United FC). This gives the agent all necessary context to supply the parameter correctly.

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 a specific verb and resource: 'Get a team's strength of schedule based on opponents already faced.' It further defines the output as average points-per-game plus a per-opponent breakdown, distinguishing it from sibling tools like get_standings or get_team_schedule.

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

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

The description gives explicit usage context: 'Useful early in the season for "who has played the tougher schedule so far?" questions.' It does not mention exclusions or alternatives explicitly, but the context is clear enough for 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds context about returns ('full team information including display name, abbreviation, and location') but does not disclose error behavior or rate limits. This is comparable to the reference example for a read-only tool.

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

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

The description is short, front-loaded with the purpose, and contains no filler. The Args block clearly documents the parameter format and origin.

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 single-parameter read-only tool with an output schema and rich annotations, the description fully covers the necessary context: what it does, how to obtain the input, and what it returns. No 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?

While the schema only says 'Team Id', the description explains it is an ESPN numeric team ID, gives an example, and specifies that it comes from get_teams. This fully compensates for the 0% schema coverage.

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

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

The description uses a specific verb and resource ('Get details for a specific MLS team'), and distinguishes itself from the sibling get_teams by emphasizing 'specific' and referencing the numeric ID. The return content is also previewed.

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

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

It explicitly states to use the numeric ID returned by get_teams, which both names the sibling tool and establishes a prerequisite. This makes the appropriate usage context clear.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. Description adds 'active' and return fields (numbered list with ID, name, abbreviation, city), which is useful context beyond the structured metadata. It doesn't mention ordering or pagination, but the output schema covers return structure.

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

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

Three sentences, each earning its place: purpose, returned fields, and follow-up usage. Front-loaded with the primary action and immediately actionable.

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

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

For a zero-parameter list endpoint with a rich output schema and strong annotations, the description provides purpose, output contents, and a pointer to the relevant sibling. Nothing important is missing for an agent to invoke it correctly.

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 schema coverage is trivially 100%. Baseline for 0 params is 4; the description correctly omits parameter details since none exist.

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 opens with 'Get all active MLS teams' – a specific verb, resource, and scope. It distinguishes from sibling get_team by noting that detailed specific-team information is available via get_team. Clearly states what output includes (ID, name, abbreviation, city).

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?

States the use case: retrieving all active teams. Explicitly directs the agent to use get_team with the ID or abbreviation for detailed information, providing a clear alternative for a common follow-up. No exclusions needed for a zero-parameter list tool.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds meaningful behavioral context by detailing the return fields (opponent, date, score if played, status) and the scope (current season, all matches). This goes beyond the annotations, though it stops short of describing pagination or possible edge cases like cancelations.

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 first sentence states the purpose, the second summarizes the response content, and the Args line gives parameter semantics. No sentence is wasted, and the structure is easy to scan.

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

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

Given the tool's simplicity (one parameter, output schema present, clear annotations), the description is complete. It covers what the tool returns, which team it applies to, and how to specify the team. There is no notable gap for an agent to correctly select and invoke the tool.

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

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

The input schema only provides 'team_id' as a required string with no description, leaving 0% schema coverage. The description compensates fully by explaining that it expects an ESPN numeric team ID and provides a concrete example ('18418' for Atlanta United FC). This gives the agent exact guidance for supplying the 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 opens with a specific verb and resource: 'Get all matches for a single MLS team in the current season.' This clearly identifies both the action and the scope, differentiating it from siblings like get_match_details (single match) and get_scoreboard (general scoreboard). It also enumerates what is included (scheduled, in-progress, completed) without ambiguity.

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 clearly states when to use the tool: when you need all matches for one MLS team in the current season. It does not explicitly mention alternatives or exclusions, but the context of sibling tools and the tool's name make the intended use obvious. A score of 5 would require explicit 'use this instead of X' guidance, which is absent.

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