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robobobby

mcp-norwegian-weather

by robobobby
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: current_weather retrieves current conditions, while weather_forecast provides hourly forecasts. There is no overlap or ambiguity between them, making it easy for an agent to select the appropriate tool based on the need for immediate vs. future weather data.

    Naming Consistency5/5

    Both tools follow a consistent snake_case naming pattern with a clear 'noun_adjective' structure (current_weather, weather_forecast). The naming is predictable and readable, with no deviations or mixed conventions.

    Tool Count3/5

    With only 2 tools, the server feels thin for a weather domain, as it lacks capabilities like historical data, alerts, or multi-day forecasts. While the tools cover basic current and forecast needs, the count is borderline minimal for comprehensive weather functionality.

    Completeness3/5

    The server provides core current and forecast operations but has notable gaps, such as missing historical weather data, severe weather alerts, or location search tools. Agents can work around this for basic queries, but the surface is incomplete for full weather domain coverage.

  • Average 3.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does (get current weather) but doesn't describe any behavioral traits like rate limits, authentication requirements, error conditions, response format, or data freshness. For a read operation with no annotation coverage, this leaves significant gaps in understanding how the tool behaves.

    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 communicates the core purpose, geographic scope, and data source without any wasted words. It's appropriately front-loaded with the main action and resource. Every element in the sentence serves a clear informational purpose.

    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 that there are no annotations and no output schema, the description should provide more complete context about what to expect from this tool. While it clearly states the purpose and geographic limitation, it doesn't describe the response format, potential errors, or any behavioral constraints. For a tool with no structured metadata beyond the input schema, this leaves the agent with insufficient information to use it effectively.

    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 schema description coverage is 100% (the 'location' parameter is fully documented in the schema as 'Norwegian city name or lat,lon coordinates'). The description doesn't add any parameter-specific information beyond what's already in the schema. With high schema coverage, the baseline score of 3 is appropriate since the description doesn't compensate with additional parameter context.

    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 verb ('Get') and resource ('current weather') with specific geographic scope ('for a location in Norway using MET Norway (yr.no)'). It distinguishes from the sibling 'weather_forecast' by specifying 'current' weather rather than forecast. However, it doesn't explicitly contrast with the sibling tool beyond the temporal difference.

    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 implies when to use this tool (for current weather in Norway) versus the sibling 'weather_forecast' (presumably for future predictions). However, it doesn't provide explicit guidance on when NOT to use it or mention any prerequisites or alternatives beyond the temporal distinction. The geographic limitation is clear but not framed as an exclusion guideline.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It mentions the data source ('MET Norway (yr.no)') which adds useful context about reliability and regional focus. However, it lacks behavioral details like rate limits, authentication needs, error handling, or what the forecast includes (e.g., temperature, precipitation).

    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 with zero waste. It front-loads the core purpose ('Get hourly weather forecast') and includes essential context (location scope and data source) without unnecessary elaboration.

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

    Completeness3/5

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

    Given no annotations and no output schema, the description provides basic purpose and scope but lacks details on behavior, return values, or error cases. For a simple read-only tool with full schema coverage, this is minimally adequate but leaves gaps in understanding how the tool behaves in practice.

    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%, so the schema fully documents both parameters. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., it doesn't clarify format for 'location' or typical values for 'hours'). Baseline 3 is appropriate when schema does the heavy lifting.

    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 hourly weather forecast'), resource ('for a location in Norway'), and data source ('using MET Norway (yr.no)'). It distinguishes from the sibling 'current_weather' by specifying 'hourly forecast' versus current conditions. However, it doesn't explicitly contrast with the sibling tool name.

    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 implies usage context by specifying 'hourly weather forecast' and 'Norway', suggesting this is for future predictions rather than current conditions. However, it doesn't provide explicit guidance on when to use this versus the 'current_weather' sibling tool or any exclusions.

    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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