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jspsmart

mcp-weather

by jspsmart
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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: query_weather deals with weather data retrieval, while write_note handles note creation. There is no overlap or ambiguity between these unrelated functions, making misselection highly unlikely.

    Naming Consistency3/5

    The tools use a consistent verb_noun pattern (query_weather, write_note), which is good. However, the server name 'mcp-weather' suggests a weather-focused domain, making write_note an outlier that breaks thematic consistency, though the naming style itself is uniform.

    Tool Count2/5

    With only 2 tools, the set feels too thin for the implied scope. The server name indicates a weather domain, but write_note is unrelated, leaving weather functionality underdeveloped (e.g., no forecast, alerts, or location-based queries). This minimal count does not adequately cover the expected domain.

    Completeness1/5

    The tool surface is severely incomplete. For a weather server, basic operations like getting forecasts, historical data, or location searches are missing, and write_note is irrelevant to the domain. This creates significant gaps that will cause agent failures in weather-related tasks.

  • Average 2.3/5 across 2 of 2 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 ISC License.

  • 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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It fails to confirm whether this is idempotent, what happens when a city is unknown, data freshness/staleness, or whether it requires external API availability. The term 'Query' implies read-only but lacks explicit confirmation.

    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 a single short sentence with no structural waste, but its brevity reflects under-specification rather than efficient communication. It front-loads the action but lacks the substantive details required to make the sentence 'earn its place'.

    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?

    Despite having only one parameter and no output schema, the description is incomplete. It omits what constitutes 'weather' data (temperature, humidity, wind), expected response structure, and error handling scenarios, leaving critical gaps for an agent attempting to use the tool effectively.

    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 input schema has 100% description coverage for the single 'city' parameter. The description adds no semantic details (e.g., examples like 'London, UK', format expectations, or disambiguation for duplicate city names), meriting the baseline score for high schema coverage.

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

    Purpose2/5

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

    The description 'Query the weather' is tautological, essentially restating the tool name 'query_weather'. It fails to specify what weather data is returned (current conditions, forecast, alerts) or the scope of coverage, though it implicitly distinguishes from the sibling 'write_note' by suggesting a read operation.

    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 is provided on when to use this tool versus alternatives, prerequisites (such as needing specific city name formats), or error conditions (e.g., city not found). The description offers no 'when-not-to-use' boundaries.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden of behavioral disclosure but fails significantly. It does not explain persistence characteristics, storage location, whether the operation is idempotent, potential error conditions, or what constitutes a 'note' in this system.

    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?

    At three words, the description is extremely brief, but this borders on under-specification rather than efficient conciseness. It is front-loaded but fails to earn its place by providing actionable context beyond the tool name itself.

    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?

    Despite low parameter complexity (1 param) and good schema coverage, the description is inadequate for a write operation with no annotations and no output schema. It omits critical context about where notes are stored, retrieval mechanisms, and success/failure indicators.

    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 input schema has 100% description coverage for the 'content' parameter. The description adds no parameter-specific guidance, but per scoring guidelines, the baseline is 3 when schema coverage is high (>80%) and the description does not need to compensate for coverage gaps.

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

    Purpose2/5

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

    The description 'Write a new note' essentially restates the tool name 'write_note' with minimal elaboration. While it correctly identifies the action and resource, it is tautological and fails to distinguish scope or behavior beyond the obvious.

    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 provided on when to use this tool versus alternatives, prerequisites for writing notes, or relationships to other operations (e.g., reading or deleting notes). The agent receives no context about the note lifecycle.

    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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