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adhikasp

MCP Weather Server

by adhikasp
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose, making it impossible for an agent to misselect between multiple options.

    Naming Consistency5/5

    The single tool name follows a consistent verb_noun pattern (get_hourly_weather). Since there is only one tool, there are no deviations or mixed conventions to evaluate, resulting in perfect consistency.

    Tool Count2/5

    A single tool for a weather server is too few for the apparent scope, as it only provides hourly forecasts without coverage for current conditions, daily forecasts, or other common weather data. This minimal set limits functionality and will likely cause agent failures in broader weather-related tasks.

    Completeness2/5

    The tool surface is severely incomplete for a weather domain, lacking obvious gaps such as current weather, daily forecasts, alerts, or historical data. With only hourly forecasts, agents cannot perform comprehensive weather-related operations, leading to significant limitations in coverage.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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

  • This repository is licensed under The Unlicense.

  • 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 provided, the description carries the full burden of behavioral disclosure. It mentions 'Get' which implies a read operation, but fails to describe any behavioral traits such as rate limits, authentication needs, error handling, or what the forecast includes. This leaves significant gaps for a tool that likely interacts with external data.

    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, efficient sentence that directly states the tool's function without any wasted words. It is appropriately sized and front-loaded, making it easy to parse quickly.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (weather forecasting likely involves external APIs), lack of annotations, no output schema, and incomplete parameter documentation, the description is insufficient. It doesn't explain return values, error cases, or operational constraints, leaving the agent poorly informed.

    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 description adds minimal meaning beyond the input schema, which has 0% coverage. It implies the 'location' parameter is used to specify where to get the forecast, but doesn't clarify format (e.g., city name, coordinates) or constraints. With one parameter and low schema coverage, the description provides some context but doesn't fully compensate.

    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 clearly states the action ('Get') and resource ('hourly weather forecast for a location'), making the tool's purpose immediately understandable. It doesn't need to differentiate from siblings since none exist, but it could be more specific about what 'hourly weather forecast' entails (e.g., temperature, precipitation).

    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 on when to use this tool versus alternatives or in what context. The description states what it does but offers no information about prerequisites, timing, or limitations, leaving the agent without usage direction.

    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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