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Domt301

Weather MCP Server

by Domt301
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: get_weather retrieves current weather data, while get_time retrieves the current time. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tool names follow the same get_<noun> pattern with snake_case, creating a predictable and consistent naming convention.

    Tool Count3/5

    With only 2 tools, the server feels thin for its stated weather domain. While not extreme, the count is borderline and the inclusion of get_time suggests a broader utility scope than the name implies.

    Completeness2/5

    The weather domain is significantly under-covered—only current conditions are included, with no forecast, historical data, or location search. The get_time tool is also unrelated to weather, leaving obvious gaps for any weather-related workflow.

  • Average 3.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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

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  • 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?

    With no annotations, the description carries the full burden of behavioral disclosure, but it only states the action and gives no insight into error handling, return format, defaults, or side effects.

    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 that is front-loaded and to the point, with no unnecessary words or repetition.

    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 is simple, but with no output schema and no annotations, the description lacks detail about return values and usage context. It is minimally viable but leaves gaps.

    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?

    The schema provides 100% coverage for both parameters, including descriptions and a default for units, so the description adds no extra parameter meaning. Baseline 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?

    The description 'Get current weather information for a city' uses a specific verb and resource, and clearly differs from the sibling tool get_time. It immediately conveys the tool's purpose without ambiguity.

    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 about when to use this tool versus alternatives. The sibling tool get_time is not mentioned, and there is no context about suitable 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.

  • Behavior3/5

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

    No annotations are provided, so the description carries the burden of behavioral disclosure. 'Get the current time' implies a read-only, non-destructive operation, but it does not specify timezone, return format, or other behavioral details. The description is sufficient for a zero-parameter 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single sentence with no unnecessary words. It is front-loaded and conveys the tool's purpose efficiently.

    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?

    Given the tool's low complexity (zero parameters, no output schema), the description is largely complete. It states what the tool does, but omits details like timezone or return type, which might be relevant in some contexts. Still, for a simple time fetcher, it is adequate.

    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?

    The tool has zero parameters, so the baseline score is 4 per the rubric. The description does not need to explain parameter semantics, and the empty input schema is consistent with the tool's simplicity.

    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 'Get the current time' uses a specific verb ('get') and resource ('time'), clearly distinguishing it from the sibling tool get_weather. It is unambiguous and directly states the tool's function.

    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 or alternative guidance is provided. The sibling tool get_weather implies differentiation, but the description does not state when to use this over get_weather or any other tool. Usage is implied rather than explicitly guided.

    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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