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penguyen72

Weather MCP Server

by penguyen72
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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

    The two tools have clearly 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 ambiguity about which tool to use for each task.

    Naming Consistency5/5

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

    Tool Count2/5

    With only 2 tools, the server feels underpowered for a weather domain. While alerts and forecasts are core functions, there are obvious gaps like current conditions, historical data, or radar imagery that would be expected in a weather API. The minimal tool count limits the server's usefulness.

    Completeness2/5

    The tool surface is severely incomplete for a weather server. It lacks current conditions, historical weather data, radar/satellite imagery, air quality information, and marine forecasts. The two provided tools cover only narrow aspects of weather data, leaving significant gaps that will hinder agent workflows.

  • 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

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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. While it indicates this is a read operation ('Get'), it doesn't describe any behavioral traits such as rate limits, authentication requirements, data freshness, error conditions, or what the forecast includes (e.g., temperature, precipitation). For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

    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 sized and front-loaded: the first sentence clearly states the purpose, followed by a structured parameter list. There's no wasted text, and every sentence serves a purpose. It could be slightly more concise by integrating the parameter descriptions more seamlessly, but overall it's efficient.

    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 that there's an output schema (which handles return values), the description doesn't need to explain outputs. However, with zero annotation coverage and a sibling tool, the description is minimal. It covers the basic purpose and parameters but lacks context on usage, behavioral traits, or differentiation from alternatives. For a simple 2-parameter tool with output schema, this is adequate but has clear gaps.

    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 explicitly lists both parameters (latitude and longitude) and provides basic semantic context ('Latitude of the location', 'Longitude of the location'). Since schema description coverage is 0%, the description compensates by adding meaning beyond the bare schema. However, it doesn't specify format details like coordinate ranges or units, keeping it from a perfect score.

    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: 'Get weather forecast for a location.' It uses a specific verb ('Get') and resource ('weather forecast'), making the function immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'get_alerts', which likely serves a related but distinct purpose in weather data retrieval.

    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. There's no mention of the sibling tool 'get_alerts', nor any context about when a forecast is appropriate versus alerts. The description simply states what the tool does without offering usage context or exclusions.

    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 the full burden of behavioral disclosure. It states the tool 'gets' data, implying a read-only operation, but doesn't specify details like rate limits, authentication needs, error handling, or what the output contains (though an output schema exists). For a tool with no annotations, 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 appropriately sized and front-loaded. The first sentence clearly states the purpose, and the second sentence efficiently explains the parameter with an example. There's no wasted text, and the structure makes it easy to scan and understand.

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

    Completeness4/5

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

    Given the tool's low complexity (one parameter) and the presence of an output schema (which handles return values), the description is mostly complete. It covers the purpose and parameter semantics well. However, it lacks usage guidelines and behavioral details (e.g., error cases), which are minor gaps in an otherwise solid description.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The description adds substantial meaning beyond the input schema. The schema has 0% description coverage, only listing 'state' as a required string. The description compensates by explaining that 'state' is a 'Two-letter US state code (e.g. CA, NY)', providing crucial format and example details that aren't in the schema.

    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: 'Get weather alerts for a US state.' It specifies the verb ('Get'), resource ('weather alerts'), and scope ('US state'), making it easy to understand. However, it doesn't explicitly differentiate from its sibling tool 'get_forecast' (which likely provides forecast data rather than alerts), so it doesn't reach the highest score.

    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. It doesn't mention the sibling tool 'get_forecast' or clarify scenarios where alerts are preferred over forecasts (e.g., for severe weather warnings). Without such context, users must infer usage from the tool name alone.

    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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