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Zachwitte21

Weather MCP Server

by Zachwitte21
OverviewSchemaRelated ServersScoreDiscussions
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

    The two tools have completely distinct purposes: get_alerts retrieves weather alerts for US states, while get_forecast provides forecasts for geographic coordinates. There is no overlap in functionality or target data, making them easily distinguishable.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'get_' prefix (get_alerts, get_forecast). The naming is perfectly uniform and predictable across the tool set.

    Tool Count2/5

    With only two tools, this server feels severely under-scoped for a weather domain. A weather server should ideally include tools for current conditions, historical data, radar, or multiple forecast types, making this minimal set inadequate for comprehensive weather interactions.

    Completeness2/5

    The tool surface is highly incomplete for a weather server. It lacks fundamental operations like getting current conditions, historical weather, radar imagery, or air quality data. Agents will face significant gaps when trying to perform common weather-related tasks.

  • Average 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

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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

  • 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 only states the basic action without mentioning critical traits like whether this is a read-only operation, potential rate limits, error handling, or what the forecast includes (e.g., temperature, precipitation). This leaves significant gaps in understanding the tool's behavior.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is front-loaded with the core purpose in the first sentence, followed by a clear 'Args:' section. It's appropriately sized with no wasted words, though it could be slightly more structured by integrating the parameter details more seamlessly.

    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 the tool's moderate complexity (2 required parameters) and the presence of an output schema, the description is minimally adequate. It covers the basic purpose and parameters but lacks behavioral context and usage guidelines, making it incomplete for optimal agent decision-making despite the output schema handling return values.

    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 explicitly lists and describes the two parameters (latitude and longitude), adding meaning beyond the input schema, which has 0% description coverage. However, it doesn't provide details like valid ranges, coordinate systems, or examples, so it only partially compensates for the schema's lack of descriptions.

    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 tool's purpose with a specific verb ('Get') and resource ('weather forecast for a location'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'get_alerts', which might also relate to weather data, leaving some ambiguity about when to choose one over the other.

    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 alternatives. It lacks context about prerequisites, such as whether the location must be valid or if there are any usage limits, leaving the agent without clear usage instructions.

    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 the full burden of behavioral disclosure but offers minimal information. It states what the tool does but doesn't describe response format, error conditions, rate limits, authentication needs, or whether it's a read-only operation. While 'Get' implies reading data, the description lacks the behavioral details needed for confident tool invocation.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is appropriately concise with two sentences that directly address purpose and parameters. The structure is front-loaded with the core functionality, followed by parameter details. While efficient, the separation with 'Args:' creates minor formatting inconsistency but doesn't significantly impact readability.

    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 the tool's moderate complexity (single parameter, no annotations, but with output schema), the description is minimally adequate. The presence of an output schema means return values don't need explanation in the description, but the description lacks context about what 'weather alerts' includes, data sources, or typical response structure. It meets basic requirements but leaves gaps in operational understanding.

    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 description adds significant value beyond the input schema, which has 0% description coverage. It explains the 'state' parameter as 'Two-letter US state code (e.g. CA, NY)', providing crucial format guidance that the schema lacks. For a single parameter tool with no schema descriptions, this compensation is effective, though it could mention validation rules or error handling for invalid codes.

    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 tool's purpose with a specific verb ('Get') and resource ('weather alerts for a US state'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'get_forecast', which likely provides different weather data. The description avoids tautology by explaining what the tool does rather than just restating the name.

    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. While it mentions the sibling tool 'get_forecast' exists in the context, the description itself contains no comparison, prerequisites, or exclusion criteria. Users must infer usage from the tool name and description alone without explicit guidance.

    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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