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HumairaShaista

weather-learning-server

by HumairaShaista
OverviewSchemaRelated 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

    With only one tool, there is no risk of confusion between tools. The tool's purpose is clearly defined and distinct by default.

    Naming Consistency3/5

    The single tool uses a clear verb_noun pattern (get_current_weather), but with only one tool, consistency cannot be meaningfully evaluated across a set.

    Tool Count2/5

    A server named 'weather-learning-server' with only one weather tool feels incomplete for learning purposes. A 'learning' server typically benefits from multiple tools (e.g., forecast, history, alerts) to cover educational use cases.

    Completeness2/5

    The server only provides current weather data, missing obvious complementary tools like forecasts, historical data, or weather alerts. This severely limits its usefulness for weather-related learning or applications.

  • Average 4.3/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 2 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

  • Behavior4/5

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

    No annotations are provided, so the description carries the full burden. It discloses that data comes from Open-Meteo, that it's real-time (not forecast or invented), and lists the response fields. It does not mention rate limits or availability constraints, which would be helpful.

    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 very concise: two sentences. The first sentence states the core purpose, and the second adds key details in a natural flow. No wasted words.

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

    Completeness5/5

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

    Given the tool has an output schema, the description doesn't need to explain return values. It also covers the important semantic clarifications (real-time, provider, disambiguation hints). For a simple 3-param weather tool, this is complete.

    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%, so baseline is 3. The description restates that city is required and that state_or_region and country help disambiguate, but does not add new format or usage details 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 clearly states 'Get live/current weather for a city' with specific verb and resource. It explicitly distinguishes itself from non-forecast and non-invented weather, and mentions the provider (Open-Meteo).

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

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explains when to use (for current real-time conditions) and notes when to add optional parameters for disambiguation. It doesn't explicitly state when NOT to use or list alternative tools, but context is clear enough.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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