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yang648557392

weather

by yang648557392
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Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools are clearly distinct: get_alerts retrieves weather warnings for a state, while get_forecast retrieves forecast data by coordinates. There is no overlap in their inputs or outcomes.

    Naming Consistency5/5

    Both tools follow the same 'get_noun' pattern, with get_alerts and get_forecast. The naming is predictable and consistent.

    Tool Count3/5

    With only two tools, the server is minimal and borderline scoped. While this could be fine for a specialized alerts/forecast service, it feels thin for a general weather service and does not reach the 3-15 tool sweet spot.

    Completeness2/5

    A weather service would typically include current conditions, hourly/daily details, or location-based lookup beyond forecast and alerts. The absence of these leaves significant gaps for users expecting general weather coverage.

  • Average 3.2/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

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

    No annotations are present, so the description bears the burden of explaining behavior. It merely states that a forecast is returned, with no information about forecast periods, units, response shape, limitations, or coordinate conventions. An agent cannot anticipate what a call will actually do or produce.

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

    Conciseness2/5

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

    The opening sentence is concise and front-loaded, but the entire Args block is redundant with the input schema. Repeating parameter names and titles adds no meaningful value for an agent and wastes space.

    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?

    While an output schema exists, the description still does not clarify core usage details like coordinate format, forecast timeframe, units, or incompatibilities with get_alerts. The definition is sufficient only for the most basic tool routing, not for correct invocation.

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

    Parameters2/5

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

    The schema description coverage is 0%, and the description's Args section merely restates the parameter names and titled 'Latitude/Longitude'. It does not provide valid ranges, units, formats, examples, or any constraints beyond the schema itself.

    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?

    States a clear verb-resource pair ('Get weather forecast') and explicitly ties it to a location, making the tool's purpose unmistakable. It naturally differentiates from the only sibling, get_alerts, since forecasts and alerts are distinct weather products.

    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?

    There is no guidance about when to use get_forecast versus get_alerts, no scenario description, and no criteria to choose between them. The usage context is only implied by the tool name and basic purpose, not actually explained.

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

  • Behavior2/5

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

    No annotations are present, so the description bears the full behavioral burden. It simply says 'Get weather alerts' without disclosing response format, error behavior, updates, or any side effects. The output schema supplies structure, but important behavioral context is missing.

    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 condensed and efficient: a one-line purpose followed by a focused parameter explanation. There is no filler, and the core intent is front-loaded through a concise, well-structured format.

    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?

    For a one-parameter read tool, the description handles the essential invocation details—the state parameter—and the output schema covers the return shape. The only gap is the lack of a guide on when to choose get_alerts rather than get_forecast, but the tool name and purpose make the distinction reasonably clear.

    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 schema itself has no parameter descriptions, but the Args block in the tool description adds meaning by specifying 'Two-letter US state code (e.g. CA, NY)'. This gives the agent the necessary format and examples for the single parameter, though it does not cover edge cases or additional constraints.

    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 opening sentence 'Get weather alerts for a US state' clearly identifies the verb and resource. It is a specific and unambiguous statement, but it does not explicitly differentiate from the sibling tool get_forecast, leaving that distinction to the tool names.

    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?

    Usage context is implied: call this tool when weather alerts for a US state are needed. However, the description provides no explicit guidance on when to prefer get_alerts over get_forecast, nor does it mention any exclusion scenarios or alternatives.

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

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