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JuanPedroSosa

Weather MCP Server

by JuanPedroSosa
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TypeScript

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one retrieves weather alerts for a state, while the other retrieves forecasts for a location. There is no overlap in functionality or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'get_' prefix and descriptive nouns (alerts, forecast). The naming is uniform and predictable across the set.

    Tool Count2/5

    With only two tools, the server feels thin for a weather domain, lacking operations like current conditions, historical data, or radar imagery. This minimal set may limit agent capabilities.

    Completeness2/5

    The toolset is severely incomplete for weather services, missing core functions such as current weather, hourly forecasts, or severe weather details. Agents will face significant gaps in handling typical weather queries.

  • Average 2.9/5 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 GPL 3.0.

  • 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 states what the tool does but lacks details on traits like rate limits, authentication needs, response format, or error handling. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

    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 is appropriately sized and front-loaded, clearly stating the tool's purpose without unnecessary elaboration. Every word earns its place.

    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 lack of annotations and output schema, the description is incomplete. It covers the basic purpose but fails to address behavioral traits, usage context, or return values. For a tool with minimal structured data, the description should provide more comprehensive guidance to aid the agent.

    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%, with the parameter 'state' fully documented in the schema. The description adds no additional meaning beyond what the schema provides, such as examples of state codes or constraints. Baseline 3 is appropriate when the 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 verb ('Get') and resource ('weather alerts for a state'), making the purpose understandable. It distinguishes from the sibling tool 'get_forecast' by specifying alerts rather than forecasts. However, it doesn't fully differentiate scope or specificity beyond the basic distinction.

    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?

    The description provides no guidance on when to use this tool versus alternatives like 'get_forecast'. There is no mention of prerequisites, exclusions, or contextual factors that would help an agent decide between tools. Usage is implied by the purpose but not explicitly stated.

    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?

    With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal information. It doesn't mention whether this is a read-only operation, what data format the forecast returns, whether there are rate limits, authentication requirements, or any other behavioral characteristics beyond the basic function.

    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 extremely concise at just 6 words, front-loading the essential information with zero wasted words. Every element ('Get weather forecast for a location') contributes directly to understanding the tool's 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?

    For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the forecast returns (e.g., temperature, precipitation, timeframe), how results are formatted, or any behavioral constraints. The agent would need to guess about the tool's output and operational characteristics.

    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 has 100% description coverage, thoroughly documenting both latitude and longitude parameters with their constraints. The description adds no additional parameter semantics beyond what's already in the schema, so it meets the baseline expectation when schema coverage is complete.

    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 weather forecast') and target resource ('for a location'), making the tool's purpose immediately understandable. However, it doesn't differentiate from its sibling 'get_alerts' tool, which likely serves a related but distinct weather-related function.

    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?

    The description provides no guidance on when to use this tool versus its sibling 'get_alerts' or any alternative approaches. It lacks context about appropriate use cases, prerequisites, or limitations that would help an agent choose between available weather-related tools.

    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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