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zaixiamaomaoyu

weather-china-mcp

by zaixiamaomaoyu
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Server Quality Checklist

67%
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  • Latest release: v1.0.4

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: current conditions, 15-day forecast, and a concise summary combining current+today+tomorrow. No overlap that would cause confusion.

    Naming Consistency5/5

    All tools follow the exact same snake_case pattern with 'weather_' prefix followed by a noun (current, forecast, summary), making the set predictable and easy to navigate.

    Tool Count5/5

    Three tools is perfectly scoped for a weather service covering current, forecast, and summary data. Not too many or too few.

    Completeness4/5

    Covers the core weather information needs (current, forecast, concise summary). Minor missing features like weather alerts or historical data, but not significant for typical usage.

  • Average 3.4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 9 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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 provided. Description only states 'get 15-day forecast' with no disclosure of data freshness, limitations, or error behavior.

    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?

    Concise with two segments: purpose and parameter. Efficient but could add structure like usage notes.

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

    Completeness2/5

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

    Simple tool with one parameter, no output schema, and minimal description. Lacks details on forecast content or how to interpret results.

    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 coverage is 100%, so baseline is 3. Description repeats the city parameter without adding new semantics 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?

    Clearly states verb 'get' and resource '15-day weather forecast from China Weather Network'. Distinct from siblings 'weather_current' and 'weather_summary'.

    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 on when to use this tool versus siblings or any context for scenario selection.

    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 the full burden. It only mentions the data scope (real-time + today + tomorrow) and format (concise), but lacks details on data freshness, source reliability, or any operational constraints.

    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 a single sentence plus a parameter note, front-loading the core purpose and providing essential context with no extraneous text.

    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 tool has no output schema, and the description does not detail the return format or fields. While it states '简洁格式' (concise format), this is vague. For a simple tool with one parameter, the description is adequate but could be improved by specifying what data points are included in the summary.

    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?

    There is only one parameter, 'city', with schema description fully covering its semantics (e.g., example city names). The description reiterates '参数：city（城市名）' but adds no additional meaning beyond the schema. With 100% schema coverage, a baseline score of 3 is appropriate.

    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?

    Description clearly states the tool retrieves a weather summary from China Weather Network, covering real-time, today, and tomorrow in concise format. This distinguishes it from siblings weather_current and weather_forecast, which focus on single aspects.

    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 the tool is for retrieving a combined summary when both current and short-term forecast are needed, but does not explicitly state when to use it versus siblings or provide exclusion criteria.

    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 provided, so the description carries full burden. It lists returned attributes but does not mention behavior like rate limits, authentication needs, or response format. Acceptable for a simple read-only tool but lacks depth.

    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?

    Single sentence containing both purpose and parameter info. Concise and front-loaded, though slightly redundant with the schema. No wasted words.

    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 no output schema and simple input, the description lists key output attributes (temp, humidity, PM2.5, etc.) but doesn't specify structure or units. Adequate but could be more precise.

    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% (one parameter 'city' with examples). The description repeats the parameter with an example ('广州'), adding no extra meaning beyond the schema. Baseline score of 3 is appropriate.

    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?

    Description clearly states it retrieves real-time weather data (temperature, humidity, PM2.5, etc.) from China Weather Network. It names specific attributes and distinguishes from sibling tools (weather_forecast, weather_summary) by focusing on current conditions.

    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?

    No explicit guidance on when to use vs. alternatives. The sibling tool names imply 'current' vs. 'forecast' or 'summary', but the description does not clarify use cases or exclusions.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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