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Latinum-Formal-Methods

Weather MCP Tool

by Latinum-Formal-Methods
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Python
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Server Quality Checklist

50%
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  • Latest release: v0.0.1

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to confuse it with. The single tool's purpose is clearly defined as retrieving weather information.

    Naming Consistency5/5

    Since there is only one tool, it inherently exhibits perfect naming consistency with itself. The tool name 'get_weather' follows a clear verb_noun pattern, which would be consistent if more tools existed.

    Tool Count2/5

    A single tool for a weather server is too few for the apparent scope, as weather-related operations typically involve more functionality (e.g., forecasts, alerts, historical data). This minimal set feels thin and incomplete for the domain.

    Completeness1/5

    The tool surface is severely incomplete for a weather domain, offering only basic current weather retrieval. There are significant gaps, such as lack of forecast data, location-based searches, or historical weather information, which will likely cause agent failures in broader tasks.

  • Average 1.1/5 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

  • Behavior1/5

    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. However, it reveals nothing about the tool's behavior—whether it's a read-only operation, requires authentication (implied by 'signed_b64_payload'), has rate limits, or what the output might be. This leaves critical operational traits completely opaque.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

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

    The description is extremely brief ('Call self as a function.'), which might seem concise, but it's under-specified rather than efficient. It wastes its single sentence on a tautology that provides no value, failing to front-load useful information or structure content meaningfully for the agent.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness1/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (2 parameters, no output schema, no annotations), the description is completely inadequate. It lacks purpose, usage, behavioral details, parameter explanations, and any mention of return values, making it insufficient for an agent to understand or invoke the tool correctly in any context.

    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?

    The input schema has 2 parameters with 0% description coverage, meaning no parameter details are documented in the schema. The description adds no semantic information about parameters like 'city' (e.g., format, examples) or 'signed_b64_payload' (e.g., purpose, when required), failing to compensate for the schema's lack of documentation.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose1/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Call self as a function' is a tautology that merely restates the tool's name 'get_weather' in abstract terms, providing no information about what the tool actually does. It fails to specify the verb (e.g., retrieve, fetch) or resource (e.g., weather data, forecasts), making it completely unhelpful for understanding the tool's purpose.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines1/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description offers no guidance on when to use this tool, such as for checking current weather, forecasts, or specific conditions. With no sibling tools mentioned, there are no alternatives to differentiate from, but the description still provides zero context or prerequisites for usage, leaving the agent with no actionable information.

    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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