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bhujangarao-db

weather-mcp-server

by bhujangarao-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 confusing it with another. The tool's purpose is clearly defined and distinct.

    Naming Consistency5/5

    The single tool uses a clear, conventional verb_noun pattern (get_weather), which is consistent and intuitive. There are no naming inconsistencies to evaluate.

    Tool Count3/5

    A single tool for a weather server is minimal but borderline acceptable because it combines current conditions and forecast. However, it feels thin compared to the typical scope of a weather-focused MCP server.

    Completeness4/5

    The tool covers the core weather needs—current conditions and forecast—with configurable forecast days. Missing features like alerts or historical data are minor gaps that agents can work around for basic weather queries.

  • Average 3.9/5 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.

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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 behavioral burden. It communicates a read-only retrieval operation and the scope of returned data, but it does not disclose data source assumptions, units, timezone/freshness behavior, or error handling. This is adequate for a simple read tool but not deeply transparent.

    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 compact and well-structured: a one-sentence main behavior followed by two focused argument descriptions. Every sentence earns its place, and the behavioral summary is front-loaded.

    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 available, the description covers the essential call semantics. The main omissions are environmental details like units, timezone, and data freshness, which are not strictly required for invoking the tool correctly.

    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 description coverage is 0%, and the description fully compensates. It explains that location is a city, region, or country name with concrete examples, and it clarifies forecast_days as a 1-through-7 range, adding real meaning beyond the bare string/integer schema types.

    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 states a specific verb and resource: 'Get current conditions and a weather forecast for a place.' It clearly distinguishes the output scope (current conditions plus forecast), though it does not need to differentiate from siblings because none are listed.

    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 context is implied: an agent would use this tool when current weather or a forecast is needed for a location. However, there is no explicit when-to-use vs alternatives, no exclusions, and no edge-case guidance such as ambiguous location handling.

    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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