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nitvob

Weather MCP Server

by nitvob
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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 clearly distinct purposes: one for weather alerts by state, one for forecast by coordinates. No overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow the consistent 'get_<resource>' pattern (get_alerts, get_forecast), with clear nouns indicating the resource type.

    Tool Count3/5

    With only 2 tools, the server feels minimal but adequately covers its stated purpose of weather alerts and forecasts. It is at the lower bound of acceptable scope.

    Completeness3/5

    The server covers alerts and forecasts, but lacks current conditions, historical data, or other common weather queries. Basic coverage, but notable gaps exist.

  • 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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  • 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 must disclose behavior but only states the basic function. It does not mention data freshness, units, forecast period, or any limitations, which is insufficient for a forecast tool.

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

    Conciseness3/5

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

    The description is short but includes an 'Args' block that redundantly restates schema parameters. It could be more concise without this structure, but overall it is not overly verbose.

    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 simple tool with output schema, the description misses important context: whether it returns current or forecast data, time resolution, geographic coverage, or usage notes. The sibling tool further demands differentiation.

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

    Parameters2/5

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

    Schema coverage is 0%, so the description must add value. It repeats parameter names and adds minimal context ('Latitude of the location', 'Longitude of the location'), but lacks details like valid ranges or format.

    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 retrieves a weather forecast for a location using the verb 'Get' and resource 'weather forecast'. It effectively distinguishes from the sibling 'get_alerts', which presumably deals with alerts.

    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?

    There is no guidance on when to use this tool versus alternatives like 'get_alerts'. No exclusions or prerequisites are mentioned, leaving the agent to infer usage from context.

    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, and the description does not disclose behavioral traits such as rate limits, data sources, or that it is a read-only operation. The minimal description leaves the agent to infer behavior from the tool name alone.

    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 very short, but it includes a clear 'Args' section. While the parameter details are repeated from the schema, the added examples make it useful. A little more conciseness could be achieved by dropping the 'Args' section if unnecessary, but it's acceptable.

    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 has an output schema (unspecified) and only one parameter, the description is somewhat minimal. It adequately covers the input but does not explain the output format or how alerts differ from forecasts. More context would improve completeness.

    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 input schema has no description for the 'state' parameter, but the description adds valuable meaning: it specifies a two-letter US state code with examples (CA, NY). This compensates for the 0% schema description coverage.

    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 gets weather alerts for a US state, which is a specific verb-resource combination. It distinguishes itself from the sibling tool 'get_forecast' by focusing on alerts rather than forecasts.

    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 is for US state alerts but does not provide explicit guidance on when to use this tool versus alternatives like 'get_forecast'. No when-not-to-use or prerequisite information is given.

    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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