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VenkatAdena

Global Weather MCP Server

by VenkatAdena
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

  • Disambiguation3/5

    get_alerts is distinct for US alerts, but get_forecast and get_forecast_global both provide forecasts; their regional scope is unclear from descriptions, causing potential confusion.

    Naming Consistency5/5

    All tools follow a consistent verb_noun snake_case pattern (get_alerts, get_forecast, get_forecast_global).

    Tool Count3/5

    With only 3 tools, the server is minimal but covers basic weather needs; it's borderline but not extreme.

    Completeness3/5

    Covers alerts and forecasts, but lacks current conditions, historical data, and other common weather features, leaving notable gaps.

  • Average 3.5/5 across 3 of 3 tools scored. Lowest: 2.6/5.

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

    • No community issues in the last 6 months
    • 2 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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. It only states the action (get) but does not disclose behavioral traits such as data freshness, units, rate limits, or whether it is read-only (implicitly yes but not explicit). Minimal transparency.

    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 very short and front-loaded with the purpose. However, it is under-specified for a tool with two parameters and no annotation support. It could include more context without losing conciseness.

    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 simplicity (2 parameters, output schema exists), the description covers the basic purpose and parameters. But it lacks usage context, error conditions, or clarification on output (though output schema covers that). Adequate but not complete for an agent to use without ambiguity.

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

    Parameters1/5

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

    Schema coverage is 0% (no descriptions in schema), so the description must compensate. It merely restates parameter names ('latitude: Latitude of the location') without adding format, units, valid ranges, or any extra meaning. This provides negligible value beyond 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 gets a weather forecast for a location via 'Get weather forecast for a location.' This differentiates from sibling 'get_alerts' (alerts vs forecast) and implies location-specific vs 'get_forecast_global'. However, it does not explicitly call out the differentiation.

    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?

    No guidance on when to use this tool versus siblings (e.g., get_alerts or get_forecast_global). The description only states what it does, not when or when not to use it.

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

  • Behavior4/5

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

    With no annotations, the description discloses that the tool uses a free API requiring no authentication, which is positive. However, it does not mention rate limits, error handling for invalid coordinates, or response structure. Still, it provides useful behavioral context.

    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: two short paragraphs with no unnecessary words. It front-loads the purpose and includes essential usage notes. Every sentence is earned.

    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 low complexity (2 params, no nested objects) and the presence of an output schema, the description is fairly complete. It explains the API source and coordinate examples. Minor gaps like timezone or units are acceptable for a simple forecast tool.

    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 0% description coverage, so the description's 'Args' section adds value by providing example values (e.g., 28.6139 for New Delhi). This compensates for the lack of schema descriptions and clarifies the coordinate format.

    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 'Get weather forecast for any location worldwide including India', which specifies the verb and resource. The name 'get_forecast_global' and the sibling 'get_forecast' hint at a regional distinction, but the description does not explicitly differentiate usage, so it's not a 5.

    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 mentions it uses the free Open-Meteo API with no API key, but does not provide explicit when-to-use or when-not-to-use guidance relative to siblings. This is minimal but adequate.

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

  • Behavior3/5

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

    No annotations provided; description is minimal. Does not disclose behavioral aspects like idempotency, rate limits, or security. However, 'get' implies read-only, so borderline acceptable.

    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?

    Extremely concise: two sentences. Purpose is stated first, then parameter description. No wasted words.

    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 simplicity (one parameter, output schema present), the description is nearly complete. Could mention the expected output type or that alerts are for severe weather, but not necessary.

    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?

    Parameter 'state' is described with format 'Two-letter US state code' and examples, which adds significant meaning beyond the schema's type string.

    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?

    Clear verb 'Get' and resource 'weather alerts' for a specific scope 'US state'. Distinguishes from siblings get_forecast and get_forecast_global which likely provide forecasts, not alerts.

    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?

    No explicit guidance on when to use this vs alternatives. The name and description imply alerts for warnings, but no direct comparison or when-not.

    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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