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jyotsna1357

open-meteo-mcp

by jyotsna1357
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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools are clearly separated by time: get_forecast for future days and get_current for the present moment. Each description explicitly references the other to prevent confusion.

    Naming Consistency5/5

    Both tool names follow the exact same verb_noun pattern: get_ + forecast/current. This is perfectly consistent and predictable.

    Tool Count3/5

    With only 2 tools, the server feels thin but not unusable. For a focused weather-with-current-and-forecast scope, it is borderline acceptable, though a richer API would typically include more.

    Completeness4/5

    The two tools cover the primary current and short-term forecast use cases. Missing historical data and weather alerts are notable gaps, but for many queries these tools are sufficient.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 9 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • 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

  • Behavior4/5

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

    With no annotations, the description carries the behavioral disclosure burden. It states what output to expect (conditions, temperature, feels-like, humidity, wind, recent precipitation) and notes that measurements are 'measured at the city's local time.' It does not mention error handling or units, but for a simple read-only weather tool, it provides sufficient transparency.

    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 three sentences, each earning its place: purpose, what you get, and when to use an alternative. It is front-loaded with the main action and avoids fluff.

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

    Completeness5/5

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

    For a simple one-parameter tool with no output schema and no annotations, the description is remarkably complete. It covers what data is returned, when to use the tool, and the alternative for other time frames. No critical information seems missing.

    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?

    Schema description coverage is 100% — the 'city' parameter has a detailed description ('written the way a person would say it'). The tool description adds no extra parameter information, so the baseline of 3 applies.

    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's purpose: 'Find out what the weather is like in a city right now.' It specifies the resource (city weather) and the verb (find out/get current), and explicitly distinguishes from the sibling by advising 'If you need tomorrow or the days after, use get_forecast instead.'

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides explicit usage context: 'Use this for questions about the present moment' and gives examples of when it's appropriate. It also names an alternative tool for future forecasts, giving clear when-to-use vs. when-not-to-use guidance.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

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

    With no annotations, the description carries the burden and does well: it explains the output format (one entry per day with conditions, temps, rain, wind) and the forecast horizon. It doesn't mention potential limitations or side effects, but for a read-only forecast, no such concerns are expected, so this is acceptable.

    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 front-loaded. It uses three sentences to cover purpose, usage examples, and the alternative tool, with no filler. Every sentence contributes value.

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

    Completeness5/5

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

    The description fully compensates for the missing output schema by summarizing the response structure. It also provides semantic context (daily entries, fields) and clear sibling differentiation. For a simple 2-parameter tool with a rich schema, nothing essential is missing.

    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 input schema covers 100% of parameters with detailed descriptions. The tool description adds a little extra context about output entries and the usable day range, but it largely reiterates what the schema already conveys, so it doesn't significantly elevate beyond the schema baseline.

    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's function ('Look up the daily weather forecast for a city') and scope (1-7 days ahead). It explicitly distinguishes from the sibling tool get_current by directing users there for current conditions, making the purpose unambiguous.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Provides explicit when-to-use guidance with concrete examples ('what tomorrow looks like, whether the weekend will be dry') and names the alternative for real-time conditions ('use get_current instead'). This fully addresses usage context and selection among siblings.

    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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