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Weather MCP Server

Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    get_alerts and get_forecast have clearly distinct purposes: one returns alerts for a US state, the other returns a forecast for coordinates. There is no ambiguity between the two.

    Naming Consistency5/5

    Both tools follow the same get_ noun pattern, making the naming predictable and consistent. The singular/plural difference (forecast vs. alerts) is semantically appropriate.

    Tool Count3/5

    With only two tools, the server feels thin for a weather domain, but it is still within a borderline range. Each tool provides a distinct core weather function, so the count is not unreasonable.

    Completeness2/5

    The weather domain typically includes current conditions, forecasts, and alerts. This server lacks current weather data and only handles forecasts and alerts, which is a significant gap for general weather use.

  • Average 3.1/5 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 is passing
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  • This repository includes a README.md file.

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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?

    There are no annotations, so the description must carry behavioral context. It only says 'get' a forecast and gives no indication of units, time range, coordinate format, or whether this is a read-only operation. Nothing contradicts annotations, but little is disclosed beyond the basic action.

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

    Conciseness4/5

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

    The description is short and the core purpose is front-loaded. The Args block is somewhat redundant with the schema but does not add significant bloat, keeping the overall entry compact.

    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?

    The description provides the essential call requirements (latitude and longitude) and the presence of an output schema reduces the need to document return values. However, it omits practical context like expected coordinate units, available forecast periods, and why an agent would choose this over get_alerts.

    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 Args section repeats the parameter names with minimal glosses ('Latitude of the location'), adding almost no meaning beyond the schema titles. Since schema description coverage is 0%, the description should compensate with coordinate format or range details, but it does not.

    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 uses a clear verb and resource: 'Get weather forecast for a location'. It does not explicitly mention the sibling get_alerts, but the forecast-vs-alerts distinction is clear enough from the domain.

    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?

    No guidance is given about when to use this tool instead of get_alerts, nor are any exclusions or alternative conditions provided. The intended usage is only implied by the tool name and description.

    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 provided, so the description carries full behavioral disclosure burden. It only states the action and scope, with no mention of alert types, applicable time windows, data source behavior, error handling, or response characteristics.

    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?

    Extremely concise: a short declarative sentence followed by the parameter explanation. No wasted words, and the key purpose is front-loaded.

    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?

    For a simple one-parameter tool with an output schema, the core invocation information is present. However, the absence of usage guidance and behavioral details leaves some context gaps, especially given no annotations are available.

    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%, but the description compensates well for the single parameter: it explains state is a two-letter US state code and gives examples. This adds meaningful value beyond the schema's bare string type, though it does not enumerate all valid state codes.

    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?

    States a specific action ('Get weather alerts') and resource ('weather alerts') scoped to a US state. It is clearly distinct from the sibling get_forecast, though it does not explicitly name or contrast that sibling.

    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 this tool is for alerts rather than forecasts, but provides no explicit guidance on when to choose this tool over get_forecast or any other alternative. The usage context is largely left to inference from the tool name and description.

    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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  • Evaluate tool definition quality.

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