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TheHexaflux

@xjt-demo/xjt-weather-mcp

by TheHexaflux
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.1.0

  • Disambiguation5/5

    Each tool targets a clearly distinct weather data type: forecast (future days) vs live (current conditions), with no overlap.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with snake_case: get-weather-forecast and get-weather-live.

    Tool Count3/5

    Two tools is minimal but covers the core weather needs (forecast and live). Could be expanded with alerts or historical data.

    Completeness3/5

    Covers forecast and live conditions, but lacks extras like air quality, UV index, or severe weather warnings that a comprehensive weather service might offer.

  • Average 3.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 19 commits in the last 12 weeks
    • Last stable release on
    • 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 exist, so the description must fully disclose behavior. It only states it returns 'real-time weather' but omits key traits like what happens on invalid input, error responses, or any side effects. The description is insufficient for safe invocation.

    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 with two short sentences, delivering essential information without any fluff. Every word serves a purpose.

    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 simplicity (one parameter, no output schema), the description covers the basic purpose and input. However, it lacks usage guidance and behavioral details, which are important for correct invocation. It is minimally adequate but not fully 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 the schema already documents the 'city' parameter. The description repeats the same info (city name or 6-digit adcode) without adding new semantic meaning like valid formats or examples, resulting in no added value 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?

    Description clearly states '获取指定城市的实况天气' (get real-time weather for a specified city) with data source '高德地图' (Amap). The verb 'get' and resource 'live weather' are specific, and the sibling tool 'get-weather-forecast' indicates this tool is for live data, distinguishing it well.

    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 the sibling 'get-weather-forecast'. The description only explains parameter input format, not usage context or conditions. This lack of differentiation leaves the agent uncertain about which tool to call.

    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?

    Discloses the time range (today + 3 days) and data source (Gaode map). However, no annotations are provided, and the description does not mention other behavioral traits such as update frequency, units, or error handling. With no annotations, the description carries the full burden but only partially fulfills it.

    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: two short sentences that front-load the main purpose and scope. Every word adds value; no redundancy or filler.

    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 tool with one parameter and no output schema, the description covers the essential aspects: input format, output time range, and data source. It is nearly complete, though lacking details on output structure or error conditions. Given the tool's low complexity, this is adequate.

    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%, and the description's mention of city name or adcode matches the schema's own description. The description adds no new semantic information beyond what the schema already provides, so 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?

    Clearly states the tool retrieves a weather forecast for a specified city, including today and next 3 days, using Gaode map data. The name and description effectively distinguish it from the sibling 'get-weather-live' (current weather).

    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?

    Mentions acceptable input formats (city name or 6-digit adcode) but provides no explicit guidance on when to use this tool versus 'get-weather-live'. The distinction is implied by 'forecast' vs 'live' but not explicitly stated.

    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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