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suryanshp1

Weather MCP Server

by suryanshp1
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Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clearly defined and distinct by default.

    Naming Consistency5/5

    A single tool inherently follows a consistent naming pattern. The tool name 'get_alerts' uses a clear verb_noun format, which would be consistent if more tools were added.

    Tool Count2/5

    A single tool is too few for a weather server, as it lacks basic operations like getting current conditions, forecasts, or historical data. This severely limits the server's utility and scope.

    Completeness1/5

    The tool set is severely incomplete for a weather domain. It only provides alerts for US states, missing essential functions such as retrieving current weather, forecasts, or data for locations outside the US, making it inadequate for typical weather-related tasks.

  • Average 2.9/5 across 1 of 1 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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool 'Get weather alerts' but does not describe behavioral traits such as rate limits, authentication needs, error handling, or what the return format looks like. For a tool with no annotations, this is a significant gap in 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 appropriately sized with two sentences, but it is not optimally structured. The first sentence states the purpose, and the second explains the parameter, but it could be more front-loaded with key details. It avoids waste but lacks polish in organization.

    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 complexity (simple tool with one parameter), no annotations, and no output schema, the description is incomplete. It covers the purpose and parameter semantics but misses behavioral context, usage guidelines, and output details. For a tool with no structured support, more comprehensive description is needed.

    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 schema description coverage is 0%, so the description must compensate. It adds meaning by specifying that the 'state' parameter is a 'Two letter US state code (e.g CA, NY etc)', which clarifies the format and provides examples. This is valuable beyond the basic schema, though it could be more detailed (e.g., list of valid 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: 'Get weather alerts for US state' specifies the verb ('Get'), resource ('weather alerts'), and geographic scope ('US state'). It distinguishes the tool's function well, though without sibling tools, full differentiation isn't tested. The purpose is specific and actionable.

    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, prerequisites, or context for invocation. It only states what the tool does without indicating scenarios, limitations, or comparisons to other tools. This leaves the agent without usage direction.

    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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