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DongDong1997

Weather MCP Server

by DongDong1997
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

    Each tool addresses a distinct weather information need: current conditions, forecast, and warnings. There is no functional overlap.

    Naming Consistency5/5

    All tool names follow the 'get_<resource>' pattern (get_current_weather, get_forecast, get_warnings), ensuring predictability.

    Tool Count5/5

    Three tools cover the essential weather functions (current, forecast, warnings) without being too few or excessive.

    Completeness4/5

    Core weather data needs are met, but optional features like historical data or air quality are absent. Minor gap, but acceptable for a focused weather server.

  • 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
    • 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.

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

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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?

    The description omits behavioral details such as rate limits, data freshness, or error handling. With no annotations, the agent is left unaware of potential side effects or restrictions beyond the basic input-output expectation.

    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 extremely concise, using two sentences to cover both the purpose and parameter semantics. It is efficiently front-loaded, with every word adding value.

    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?

    While the description adequately explains the input parameters and an output schema exists, it lacks context about the forecast's temporal coverage (e.g., daily breakdown) and does not highlight differences from sibling tools. It meets minimum viability but leaves gaps.

    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 description coverage is 0%, but the description compensates well by explaining that location accepts city names, coordinates, or IDs, and that days must be one of 3/7/10/15/30. This adds essential meaning beyond the bare type definitions.

    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 clearly states it retrieves a weather forecast for a specified city, effectively communicating the core function. However, it does not explicitly distinguish this tool from siblings like get_current_weather or get_warnings, relying on the tool name and implicit context.

    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 is provided on when to use this tool versus its siblings (e.g., for short-term conditions vs. warnings). The description only lists parameters without suggesting appropriate contexts or alternatives.

    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 present, so the description carries full burden. It only states it gets current warnings, with no disclosure of data freshness, rate limits, or scope of warnings (e.g., types). Minimal behavioral context.

    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?

    Extremely concise with two sentences. The first sentence immediately states the purpose. No redundant information.

    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 output schema exists, return values are covered. However, for a simple tool, more context about data source, update frequency, or selection criteria versus sibling tools would improve completeness. Currently adequate but minimal.

    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 0%, but the description adds meaning by explaining that 'location' can be a city name, lat/lng, or Location ID. However, it does not provide examples or clarify what a Location ID is, so it only partially compensates.

    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 verb (获取/get) and resource (天气预警/weather warnings) with specificity ('当前生效中' meaning currently active). It distinguishes from sibling tools (get_current_weather, get_forecast) by focusing on warnings.

    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?

    Usage is implied (when you need weather warnings) but no explicit when-to-use or when-not-to-use guidance is provided. No alternatives are mentioned.

    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, and the description does not disclose any behavioral traits such as data freshness, accuracy, rate limits, or side effects. It only covers parameter formats.

    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 concise and front-loaded with the main purpose. The parameter documentation is structured clearly, though it follows a standard docstring format.

    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 an output schema but the description does not explain return values or behavior. Given the complexity and lack of annotations, more context on behavior would improve completeness.

    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?

    With 0% schema description coverage, the description adds significant value by detailing the three formats for location, multi-language options for lang, and metric/imperial units for unit, beyond the basic type definitions.

    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 it gets real-time weather for a specified city, using a specific verb and resource. It distinguishes from siblings like get_forecast and get_warnings.

    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 for current weather but does not explicitly state when to use this tool versus alternatives or when not to use it.

    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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