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AronSoldok

weather_mcp

by AronSoldok
OverviewSchemaSourceRelated ServersScoreDiscussions
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

    Only one tool exists, so there is no possibility of confusion or overlap. The single tool's purpose is clearly defined as a 7-day forecast by city name.

    Naming Consistency4/5

    The tool name get_forecast follows a clear verb_noun convention that would be consistent with a broader weather toolset. However, with only one tool, the naming pattern is not fully demonstrable.

    Tool Count1/5

    A single tool is far too few for a weather server, which typically requires current conditions, geocoding, unit preferences, and alerts. This feels like an extreme under-scoping of the domain.

    Completeness1/5

    The tool surface is severely incomplete for a weather service. It only provides a 7-day forecast, leaving out current weather, location search, weather alerts, and other common weather data operations.

  • Average 4/5 across 1 of 1 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

  • This repository is licensed under Apache 2.0.

  • 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

  • Behavior3/5

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

    No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose the forecast horizon and accepted input script, but it does not mention possible limitations, error behavior, timezone considerations, or data source. The description is not misleading but is 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?

    A single compact sentence that front-loads the core semantics and the input requirement. There is no redundancy or filler; every part contributes useful information.

    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 simple one-parameter tool with an output schema, the description covers the essential input and output semantics. Minor gaps like units, city ambiguity, or error behavior are not addressed, but the tool's simplicity and the presence of an output schema keep these gaps acceptable.

    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 coverage is 0%, so the description must compensate. It adds meaning by clarifying that the city value is a name (rather than an ID) and explicitly supports both Cyrillic and Latin script. This goes beyond the bare string type in the schema, though it doesn't specify additional formatting or disambiguation 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 states a clear verb and resource: it returns a 7-day forecast for a given city name. It is specific and unambiguous, and the additional script hint (Cyrillic or Latin) further sharpens the purpose.

    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?

    There are no sibling tools or explicit when/when-not guidance, but the description implies the intended use: whenever a 7-day city forecast is needed. This is adequate for a simple single-purpose tool but leaves the agent to infer the context.

    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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