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sritajkumarpatel

Weather Service MCP

by sritajkumarpatel
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Python

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 ambiguity or overlap between tools. The single tool has a clear, distinct purpose that cannot be confused with any other tool in the set.

    Naming Consistency5/5

    The single tool follows a clear verb_noun pattern (get_weather), and with only one tool, there is perfect consistency. There are no other tools to create naming conflicts or inconsistencies.

    Tool Count2/5

    A single tool is too few for a weather service domain, which typically requires operations like forecasts, historical data, or multiple location queries. This feels thin and incomplete for the apparent scope of a weather service.

    Completeness2/5

    The tool surface is severely incomplete for a weather service. While get_weather provides current conditions, there are obvious gaps such as missing forecast retrieval, historical weather data, or multi-location queries, which will limit agent capabilities in this domain.

  • 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. It states it's a mock implementation and suggests real API integration, which hints at limitations but lacks details on behavior like error handling, data freshness, or authentication needs. It does not disclose critical traits like rate limits or response format beyond a vague 'string describing conditions'.

    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 well-structured with clear sections (Args, Returns, Note) and front-loaded purpose. It is appropriately sized, but the note about mock implementation, while useful, adds length that might not be essential for tool invocation by an AI agent.

    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 low complexity and the presence of an output schema (though not detailed here), the description is minimally adequate. It covers purpose and parameters but lacks behavioral context and usage guidelines. With no annotations and simple schema, it should do more to explain expected outputs and constraints.

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

    Parameters3/5

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

    The schema description coverage is 0%, but the description compensates by explaining the 'location' parameter as 'The name of the city or location to get weather for', adding meaning beyond the bare schema. However, it does not provide examples, constraints, or format details, keeping it at a baseline level.

    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 with a specific verb ('Get') and resource ('current weather for a given location'). It distinguishes what it does (retrieve current weather) without being tautological. However, since there are no sibling tools mentioned, it cannot demonstrate differentiation 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 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 does not mention any prerequisites, constraints, or scenarios where this tool is preferred. The note about mock implementation and production integration is technical, not usage-related for an AI agent.

    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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