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00aimlds00

Weather MCP Server

by 00aimlds00
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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

    get_alerts and get_forecast have entirely distinct purposes: one retrieves weather alerts for a state, the other retrieves forecasts for coordinates. There is no overlap or potential confusion between them.

    Naming Consistency5/5

    Both tools follow the same get_noun pattern, making the naming scheme predictable and internally consistent. Even with just two tools, the convention is clear.

    Tool Count3/5

    Two tools falls into the borderline range for a weather service. While the tools are both useful, the count feels thin for a general-purpose weather server that would typically also include current conditions or location-based search.

    Completeness3/5

    The server covers alerts and forecasts, but lacks other common weather operations such as current conditions, hourly forecasts, or marine weather. These are notable gaps, though the two existing tools cover important requested functionality.

  • Average 3.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 6 commits 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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  • 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?

    No annotations are supplied, so the description must carry the full burden of explaining behavior. It only says 'Get weather forecast' with no mention of whether the operation is data-producing, side-effect-free by implication, but it also omits things like whether special credentials are needed, rate limits, error cases, or how the forecast is time-bounded. This is a meaningful gap for an unannotated tool.

    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 compact and front-loaded with the decisive sentence. The Args block is minimal and structurally clear, and there is no irrelevant filler or repetition beyond the parameter lines.

    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?

    With a simple tool, an output schema, and only two parameters, the description covers the basic invocation requirements. However, it leaves out usage distinctions and fails to clarify behavior beyond 'get forecast', which matters especially because annotations are absent.

    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 has no parameter descriptions and the description merely restates 'latitude: Latitude of the location' and 'longitude: Longitude of the location'. There is no added meaning about decimal-degree ranges, examples, units, or coordinate-system caveats, so the description fails to compensate for the low schema coverage.

    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 states a specific verb phrase 'Get weather forecast' with a clear target: a location. This makes the core purpose obvious. It does not explicitly contrast with the sibling tool get_alerts, so it stops short of fully differentiating itself.

    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 gives no guidance on when to prefer this tool over get_alerts or other weather-related options. It does not mention scenarios, limitations, or when not to use it, so the agent has to infer appropriateness from the tool name alone.

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

  • Behavior3/5

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

    No annotations exist, so the description carries the full burden. It clearly communicates a read-only, simple lookup, and the single parameter plus schema cover most of what is needed. However, it does not disclose edge behaviors such as handling of invalid state codes, empty alerts, or whether data is current or delayed, so the behavioral context remains minimal.

    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 tight, front-loaded, and has no filler. A single purpose sentence plus a parameter explanation is the appropriate size for a one-parameter tool.

    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?

    This is a low-complexity tool: one required parameter, a documented parameter format, and an output schema that bounds the return shape. The description is sufficient for invocation. The main gap is the lack of any reference to the sibling tool, but that is more relevant for usage guidance.

    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 has no string descriptions, so the 'Args' section adds useful semantics by explaining that 'state' is a two-letter US state code and provides examples (CA, NY). This meaning is necessary and helpful beyond the Schema.

    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 uses a specific verb ('Get'), a distinct resource ('weather alerts'), and a clear scope ('US state'). It likely differentiates from the sibling tool 'get_forecast' solely by the alert-vs-forecast distinction.

    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 on when to use this tool instead of 'get_forecast', nor are any exclusions or conditions given. An agent is left to infer the selection based on the tool name alone rather than on explicit instructions in the 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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