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264Gaurav

Weather MCP Server

by 264Gaurav
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Python
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to compare it to. The tool's purpose is clearly defined and distinct by default.

    Naming Consistency5/5

    Since there is only one tool, it inherently follows a consistent pattern with itself. The naming uses a verb_noun format (get_alerts), which is clear and standard, and there are no other tools to introduce inconsistency.

    Tool Count2/5

    A single tool is too few for a weather server, as it severely limits functionality. For a weather domain, agents would expect basic operations like getting current conditions, forecasts, or historical data, but this server only provides alerts for US states, making it incomplete and under-scoped.

    Completeness1/5

    The tool set is severely incomplete for a weather server. It only covers weather alerts for US states, missing core weather functionalities such as current weather, forecasts, or data for other regions. This creates significant gaps that will likely cause agent failures when broader weather queries are made.

  • Average 3.7/5 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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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 full burden. It states the tool retrieves alerts but doesn't disclose behavioral traits like whether this is a read-only operation, rate limits, authentication needs, response format, or error conditions. The description provides basic functionality but lacks critical operational 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 perfectly concise with two sentences: one stating the purpose and one explaining the parameter. Both sentences earn their place, and the structure is front-loaded with the core functionality first followed by parameter details.

    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 simple single-parameter tool with an output schema (which handles return values), the description covers the essential purpose and parameter semantics well. However, the lack of behavioral transparency (no annotations) means it doesn't fully address operational context for a tool that likely makes external API calls.

    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?

    With 0% schema description coverage and 1 parameter, the description fully compensates by explaining the 'state' parameter meaning ('Two-letter US state code'), providing examples ('e.g. CA, NY'), and clarifying the geographic constraint ('US state'). This adds significant value beyond the bare 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 as 'Get weather alerts for a US state' with specific verb ('Get') and resource ('weather alerts'), and specifies geographic scope ('US state'). However, with no sibling tools mentioned, there's no opportunity to distinguish from alternatives, preventing a perfect score.

    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 context by specifying 'for a US state', but provides no explicit guidance on when to use this tool versus alternatives, prerequisites, or exclusions. With no sibling tools, the baseline is adequate but lacks comprehensive 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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