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jayaraj-kannan

Weather MCP Server

by jayaraj-kannan
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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

    With only one tool, there is no possibility of confusing it with others. The tool's purpose is clearly stated and unambiguous.

    Naming Consistency4/5

    The single tool name follows a clear verb_noun pattern. However, with only one example, consistency cannot be fully assessed.

    Tool Count2/5

    A single tool is too few for a weather-focused server, which would typically also cover forecasts, historical data, or location-based queries. The tool provides only current conditions.

    Completeness2/5

    The tool surface is severely limited to current weather only. Obvious gaps include forecasts, severe weather alerts, historical weather, and geocoding or location disambiguation.

  • Average 4.2/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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

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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

  • 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 does disclose that the result includes temperature, humidity, and conditions, but it does not mention units, city-resolution behavior, possible errors, or whether the operation is strictly read-only. This is acceptable but minimal.

    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: one clear purpose sentence, an Args block, and a Returns block. Every sentence earns its place and the main purpose 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 one-parameter tool with an output schema available, this description is sufficient for an agent to select and invoke the tool correctly. It could add minor caveats like units or error behavior, but those are not necessary for basic correct use.

    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 JSON schema provides only the parameter title 'City' with 0% description coverage, so the description's Args section adds crucial meaning by explaining that it expects a city name and giving concrete examples. It could add disambiguation guidance for duplicate city names, but for a single required parameter it compensates well.

    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 opening sentence 'Get current weather for a city' uses a specific verb and resource and immediately communicates the tool's purpose. It also clearly identifies the input (a city) and expected output, making the tool easy to distinguish even without siblings.

    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 phrase 'current weather' makes the intended use case clear: this tool is for real-time conditions, not forecasts or historical data. There are no sibling tools and no exclusions to document, so the usage context is sufficiently clear.

    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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  • Evaluate tool definition quality.

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