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muhammadirfanmubasyir-sys

Weather MCP Server

by muhammadirfanmubasyir-sys
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

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

    Naming Consistency5/5

    The single tool follows a clear verb_noun pattern ('get_weather'), which is consistent and predictable. No naming inconsistencies exist.

    Tool Count2/5

    A weather server with only one tool is far too few. Typical weather services require multiple tools (e.g., forecast, alerts, history) to be useful, so this minimal set is inadequate for the domain.

    Completeness2/5

    The server only provides current weather data, missing essential operations like forecasts, severe weather alerts, or historical data. This significant gap will likely cause agent failures when users request information beyond current conditions.

  • 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

  • This repository is licensed under GPL 3.0.

  • 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 provided, so the description carries full burden. It indicates a read operation but omits any behavioral details such as data freshness, rate limits, or possible errors. 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.

    Conciseness5/5

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

    A single, front-loaded sentence with no wasted words. Highly concise and easily parsed.

    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?

    With one parameter, no siblings, and an output schema, the description is minimally adequate. It covers the basic purpose but misses opportunities to mention units or the scope of weather data. The output schema compensates for missing return value details.

    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?

    The schema has no description for the 'city' parameter (0% coverage). The description adds only that it is 'for a location', adding little meaning beyond the schema. The parameter's meaning is inferred but not elaborated.

    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 it retrieves current weather for a location, providing a verb and resource. It is specific enough for a tool with no siblings, though it could be more descriptive about the weather fields.

    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 or not use the tool. Without siblings, explicit alternatives are not needed, but context like ideal use cases is missing.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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