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tun0000

taiwan-weather-mcp

by tun0000
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct weather-related domain: forecasts, earthquakes, and warnings, with no overlapping functionality.

    Naming Consistency5/5

    All tools follow a consistent 'get_' prefix with snake_case, making naming predictable and uniform.

    Tool Count4/5

    Three tools cover the main weather information needs for Taiwan, though a few additional areas like current conditions might be expected.

    Completeness4/5

    The tool set covers key weather data (forecasts, earthquakes, warnings), but lacks real-time conditions or specific weather alerts beyond the broad categories.

  • Average 4.3/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

  • Behavior3/5

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

    Describes returned fields and argument range/default, but does not specify the recency window or any rate limits/auth requirements. Since no annotations, description carries full burden; missing details on time range.

    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?

    Very concise: two sentences plus parameter list. Purpose is front-loaded, no redundant information.

    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?

    Explains result fields and parameter, but does not specify time window for 'recent'. Output schema exists so return values are documented elsewhere. Still, could be more precise about recency.

    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 has no description for limit parameter, but description adds range (1–10) and default (5), significantly improving understanding beyond 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 it returns recent significant earthquake reports with specific fields (time, magnitude, depth, epicenter, intensity summary). This distinguishes it from siblings which are weather-related.

    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 when-to-use vs alternatives, but siblings are weather tools so context implies use for earthquakes. Could mention that it only includes significant earthquakes.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

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

    No annotations are provided, so the description carries full burden. It discloses the return format (three periods), the specific data fields, and the special handling for ambiguous city names. It does not mention rate limits or authentication, but for a simple query tool, this is sufficient.

    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 five sentences, front-loaded with purpose, then return data, examples, and parameter documentation. No fluff, each sentence adds value.

    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 (not shown), the description still covers the input and output adequately. It specifies the time period, data fields, and city name handling, making it complete for a simple forecast tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 0% (only type string), and the description adds rich semantics: accepted city names (Chinese and English), examples, and the behavior for ambiguous names like 新竹/嘉義 returning both city and county. This fully compensates for the lack of schema descriptions.

    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 queries the 36-hour weather forecast for a city in Taiwan, listing the returned fields (weather phenomena, rain probability, temperature range, comfort level) and handling ambiguous city names. It is a specific verb+resource and distinct from siblings like get_recent_earthquakes.

    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 does not explicitly state when to use this tool versus alternatives, but it provides context by listing examples and noting ambiguous city name behavior. It implies usage for weather forecasts but offers no exclusion criteria or comparisons to siblings.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

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

    With no annotations, the description carries the full behavioral burden. It discloses that warnings are grouped by type, includes location and time, and specifies the exact response when no warnings are active. This provides sufficient transparency.

    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 concise, front-loaded with purpose, and every sentence adds value. 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 zero parameters and an output schema (which the description complements by explaining return behavior), the description is complete. It covers the main functionality and edge case of no active warnings.

    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?

    There are no parameters, so schema coverage is 100%. Per rubric, 0 parameters yields a baseline of 4. The description adds no parameter information but none is needed.

    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 tool queries currently active weather warnings, lists examples (typhoon, heavy rain, etc.), and specifies grouping by warning type. It naturally distinguishes from siblings 'get_forecast' and 'get_recent_earthquakes'.

    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 implies usage for retrieving current warnings but does not explicitly advise when to use versus alternatives. However, given sibling names, the context is clear enough for an AI agent.

    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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