Server Quality Checklist

Tool Scores

• get-weatherC
  2.9/5.0
  Behavior2/5

  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 weather information but doesn't mention any behavioral traits such as rate limits, authentication needs, data sources, error handling, or whether it's a read-only operation. This leaves significant gaps for an AI agent to understand how to use it effectively.

  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 appropriately sized and front-loaded, consisting of a single, efficient sentence that directly states the tool's purpose without unnecessary details. Every word earns its place, making it easy for an AI agent to quickly grasp the core functionality.

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

  Completeness2/5

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

  Given the complexity of a weather tool with no annotations and no output schema, the description is incomplete. It lacks information on behavioral aspects (e.g., rate limits, errors), output format, and differentiation from siblings. While the schema covers the input well, the overall context for safe and effective use is insufficient.

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

  Parameters3/5

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

  The input schema has 100% description coverage, with the 'city' parameter well-documented in the schema itself. The description adds no additional meaning beyond what the schema provides, as it doesn't elaborate on parameter usage or constraints. According to the rules, with high schema coverage (>80%), the baseline is 3 even without param info in the description.

  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 with a specific verb ('Get') and resource ('detailed weather information'), specifying it includes current conditions and hourly forecast. However, it doesn't explicitly differentiate from sibling tools like 'get-air-quality' or 'get-forecast', which likely provide related but distinct weather data.

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

  Usage Guidelines2/5

  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 'detailed weather information' but doesn't clarify if this is for general use, real-time data, or how it compares to siblings like 'get-air-quality' (which might focus on pollution) or 'get-forecast' (which could be broader or longer-term).

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

• get-air-qualityB
  3.1/5.0
  Behavior2/5

  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 states what data is returned but doesn't cover critical aspects like rate limits, authentication needs, error handling, or data sources. For a tool with no annotations, this is a significant gap 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.

  Conciseness5/5

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

  The description is a single, efficient sentence that front-loads the core purpose. Every word earns its place, with no wasted text. It's appropriately sized for a simple tool with one parameter.

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

  Completeness3/5

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

  Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the purpose but lacks usage guidelines and behavioral details. Without annotations or output schema, it should do more to compensate, but the simplicity keeps it from being completely inadequate.

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

  Parameters3/5

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

  The schema description coverage is 100%, with the city parameter well-documented in the schema. The description adds no additional parameter information beyond implying city scope. This meets the baseline of 3 since the schema does the heavy lifting, but the description doesn't compensate with extra context.

  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 verb 'Get' and resource 'air pollution data', specifying it includes 'European Air Quality Index and pollutant levels'. It distinguishes from sibling tools (get-forecast, get-weather) by focusing on pollution rather than weather. However, it doesn't explicitly mention the sibling differentiation, keeping it at 4 rather than 5.

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

  Usage Guidelines2/5

  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-forecast or get-weather. It mentions 'any city' but doesn't specify limitations or prerequisites, such as city availability or data freshness. This leaves the agent without clear usage context.

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

• get-forecastA
  3.5/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries full burden. It mentions what data is returned but doesn't disclose behavioral traits like rate limits, authentication requirements, error conditions, or whether this is a read-only operation. The description is functional 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, efficient sentence that front-loads the core purpose and includes all essential details without waste. Every element (duration, resource, data scope) earns its place, making it optimally concise.

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

  Completeness3/5

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

  Given the tool's moderate complexity (1 parameter, no output schema, no annotations), the description covers the basic purpose and data scope adequately. However, it lacks details about behavioral aspects and doesn't help differentiate from sibling tools, making it minimally complete but with clear gaps.

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

  Parameters3/5

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

  Schema description coverage is 100%, with the city parameter well-documented in the schema. The description adds no additional parameter semantics beyond what's already in the schema, so it meets the baseline score of 3 for high schema coverage without adding value.

  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 ('Get 7-day weather forecast'), the resource ('for any city'), and the scope of data returned ('including daily temperatures, precipitation, and sunrise/sunset times'). It distinguishes from sibling tools by specifying forecast data rather than current weather or air quality.

  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 ('for any city') but doesn't explicitly state when to use this tool versus the sibling tools get-weather and get-air-quality. No guidance is provided about alternatives or exclusions, leaving the agent to infer based on the data types mentioned.

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