Server Quality Checklist

Tool Scores

• get_league_tableA
  3.7/5.0
  Behavior3/5

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

  With no annotations provided, the description carries full burden. It discloses that results are sorted by 'attacking strength (xG per 90 minutes)', which is useful behavioral context beyond basic retrieval. However, it lacks details on rate limits, authentication needs, pagination, error conditions, or whether this is a read-only operation (implied by 'Get' but not explicit). The description doesn't contradict annotations (none exist).

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

  Conciseness5/5

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

  The description is perfectly structured and concise: purpose statement first, behavioral detail second, parameter documentation third. Every sentence earns its place - no redundant information. The three-sentence format is front-loaded with the core functionality.

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

  Completeness4/5

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

  Given 1 parameter with 0% schema coverage, no annotations, but an output schema exists, the description does well. It explains the parameter semantics and sorting behavior. The output schema existence means return values don't need description. However, for a data retrieval tool, additional context about data freshness, source, or limitations would enhance completeness.

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

  Parameters4/5

  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 meaningful semantics for the single parameter: 'league: League slug, name, or ID (default: EPL)' - explaining what the parameter represents and acceptable formats. This adds substantial value beyond the bare schema. However, it doesn't provide examples of valid slugs/names/IDs beyond the default 'EPL'.

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

  Purpose4/5

  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 in a league with their xG statistics' - a specific verb ('Get') and resource ('teams in a league with xG statistics'). It distinguishes from sibling tools (list_leagues, predict_match) by focusing on team statistics rather than league listing or match prediction. However, it doesn't explicitly contrast with siblings beyond this implicit differentiation.

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

  Usage Guidelines3/5

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

  The description implies usage context through 'Returns teams sorted by attacking strength' and the league parameter, suggesting this is for analyzing team performance metrics. However, it provides no explicit guidance on when to use this tool versus alternatives like predict_match for match outcomes or list_leagues for league metadata. No when-not-to-use scenarios or prerequisites are mentioned.

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

• list_leaguesA
  4/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the full burden. It describes the output format ('IDs and slugs') but does not disclose behavioral traits such as rate limits, authentication needs, or whether the list is static or dynamic. The description is accurate 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.

  Conciseness5/5

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

  The description is a single, front-loaded sentence that directly states the tool's function and output. Every word earns its place with no redundancy or waste, making it highly efficient and easy to understand.

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

  Completeness4/5

  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 with an output schema), the description is complete enough for a list operation. It specifies the resource and output format, and the output schema will handle return values, so no additional detail is needed for basic functionality.

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

  Parameters4/5

  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%. The description does not need to add parameter semantics, so a baseline of 4 is appropriate as it efficiently states the tool's purpose without unnecessary parameter details.

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

  Purpose5/5

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

  The description clearly states the verb ('List') and resource ('all supported soccer leagues'), specifying what information is returned ('with IDs and slugs'). It distinguishes from sibling tools like 'get_league_table' (which focuses on standings) and 'predict_match' (which focuses on match predictions).

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

  Usage Guidelines3/5

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

  The description implies usage for retrieving league metadata, but does not explicitly state when to use this tool versus alternatives like 'get_league_table' for standings or 'predict_match' for predictions. No exclusions or prerequisites are mentioned, leaving usage context somewhat open-ended.

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

• predict_matchA
  4/5.0
  Behavior3/5

  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 describes the prediction model and outputs, but lacks details on limitations (e.g., accuracy, data recency), error handling, or performance traits like rate limits. It adequately covers the core behavior but misses deeper 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.

  Conciseness5/5

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

  The description is well-structured and front-loaded, starting with the core purpose and outputs, followed by a clear parameter breakdown. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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

  Completeness4/5

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

  Given the complexity of a prediction tool with no annotations but an output schema, the description is mostly complete. It explains the model, outputs, and parameters, but could improve by addressing limitations or dependencies. The output schema likely covers return values, reducing the need for detailed output explanation here.

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

  Parameters4/5

  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 each parameter's purpose with examples (e.g., 'Home team name (e.g. "Arsenal")') and specifying the default for 'league'. This goes beyond the bare schema, though it could provide more on format constraints or valid values.

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

  Purpose5/5

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

  The description clearly states the specific action ('predict soccer match outcome'), the method ('using xG-based Poisson model'), and the exact outputs (probabilities for win/draw/loss, over/under 2.5 goals, both teams to score, and top 3 most likely scores). It distinguishes itself from sibling tools like 'get_league_table' and 'list_leagues' by focusing on match prediction rather than data retrieval.

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

  Usage Guidelines3/5

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

  The description implies usage for predicting soccer match outcomes, but it does not explicitly state when to use this tool versus alternatives or provide any exclusions. There is no guidance on prerequisites or scenarios where other tools might be more appropriate, leaving usage context somewhat vague.

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