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nagesh-db

Weather MCP Server

by nagesh-db
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
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

    Only one tool exists, so there is no possibility of confusion between tools. The single tool has a clear, unique purpose.

    Naming Consistency5/5

    The tool name follows the standard verb_noun pattern (get_weather), which is clear and predictable. With only one tool, consistency is trivially maintained.

    Tool Count3/5

    A single tool feels thin for a weather-focused server, even though get_weather covers both current conditions and forecasts. The server is on the low end of acceptable scope.

    Completeness4/5

    The tool covers the core weather use cases: current conditions and multi-day forecasts. Obvious gaps like historical data or weather alerts exist, but the essential surface is present.

  • Average 4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 4 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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  ]
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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?

    No annotations are provided, so the description carries the disclosure burden. It states the tool returns current conditions and a forecast, which is useful, but it does not explain units, timezone behavior, error handling, or whether the data is real-time or cached. For a simple read-only tool this is moderate transparency, but not complete.

    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 short and informative, leading with the main purpose and then documenting parameters. It is concise but not overly sparse, though the parameter details are somewhat redundant with what a good schema could provide.

    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 two-parameter weather tool with an output schema, the description covers the core functionality and parameter semantics well. It does not specify forecast units, date formats, or error behavior, but these are less critical given the simplicity of the tool and the presence of an output schema.

    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?

    The schema has 0% description coverage, and the description fully compensates by explaining both parameters: location expects a city, region, or country name, and forecast_days specifies the number of forecast days (1-7). This added meaning goes well beyond the raw parameter names and types.

    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 retrieves current weather conditions and a forecast for a location, with a specific verb and resource. The tool name and description align, and there are no sibling tools to confuse it with.

    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 gives a clear sense of when to use the tool—whenever weather data is needed—but provides no explicit guidance on alternatives or exclusion criteria. Since there are no sibling tools, some implied usage is acceptable, but the description could still clarify typical use cases or limitations.

    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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