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ctermiii

Weather MCP Server

by ctermiii

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as fetching weather forecasts for a specified city.

    Naming Consistency5/5

    The single tool name 'get_weather' follows a clear verb_noun pattern. Since there are no other tools to compare, consistency is inherently perfect.

    Tool Count2/5

    A single tool is too few for a weather server, as it lacks operations like searching locations, getting historical data, or setting preferences. This minimal set limits functionality and may cause agent failures in broader weather-related tasks.

    Completeness2/5

    The server is severely incomplete for a weather domain. It only provides forecast retrieval, missing essential operations such as current conditions, alerts, location search, or multi-city queries, which are standard in weather APIs.

  • Average 3.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 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
  • This repository is licensed under MIT License.

  • 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 provided, so the description carries the full burden of behavioral disclosure. It mentions the forecast range and data types but lacks details on error handling, rate limits, authentication needs, or response format. For a tool with no annotations, this leaves significant behavioral gaps, though it does add some context about the forecast capabilities.

    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 a single, efficient sentence that front-loads the core purpose and includes relevant details without unnecessary elaboration. Every part contributes to understanding the tool's functionality, though it could be slightly more structured for clarity.

    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?

    Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and data types well but lacks details on behavioral aspects like error handling or response structure, which are important for a weather API tool with no annotations to guide the agent.

    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%, with clear documentation for both parameters (city and days). The description adds minimal value beyond the schema by implying the 'days' parameter relates to the 1-16 day forecast range mentioned, but it does not provide additional syntax or format details. This meets the baseline for high schema coverage.

    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 the specific action ('获取...天气预报信息' - get weather forecast information) and resource ('指定城市' - specified city). It distinguishes the tool by detailing the comprehensive data returned (temperature, wind, comfort level, precipitation, UV index, PM2.5, etc.) and the 1-16 day forecast range, making its purpose explicit and well-defined.

    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 usage by specifying the forecast range (1-16 days) and the types of information provided, but it does not explicitly state when to use this tool versus alternatives. Since there are no sibling tools mentioned, the lack of comparative guidance is less critical, but no explicit prerequisites or exclusions are provided.

    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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