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Kohei-Suzuki22

Weather MCP Server

by Kohei-Suzuki22
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: get_alerts retrieves alerts for US states, while get_forecast provides forecasts for geographic coordinates. There is no overlap in functionality or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (get_alerts, get_forecast) with the same verb style and snake_case convention. The naming is perfectly uniform and predictable.

    Tool Count2/5

    With only 2 tools, the server feels thin for a weather domain. It lacks basic operations like current conditions, historical data, or location search, making it borderline too minimal for practical use.

    Completeness2/5

    The tool surface is severely incomplete for a weather server. It misses core functionalities such as current weather, location lookup, radar data, and unit conversion, leaving significant gaps that will hinder agent workflows.

  • Average 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

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but lacks critical behavioral details such as whether this is a read-only operation, potential rate limits, authentication requirements, or what the forecast includes (e.g., temperature, precipitation). The description is minimal and doesn't compensate for the absence of annotations.

    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 concise and well-structured, with a clear purpose statement followed by a bullet-point-like 'Args' section. Every sentence serves a purpose, though it could be slightly more front-loaded by integrating the parameter info more seamlessly.

    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 moderate complexity (2 required parameters) and the presence of an output schema, the description is somewhat complete but has gaps. It covers the basic purpose and parameters but lacks behavioral context and usage guidelines. The output schema likely handles return values, reducing the burden on the description, but overall completeness is only adequate.

    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 description explicitly lists and names the two parameters (latitude and longitude) in the 'Args' section, adding semantic context beyond the input schema's 0% description coverage. However, it doesn't provide additional details like valid ranges, units, or examples, leaving some ambiguity despite covering the parameters.

    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 ('weather forecast for a location'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'get_alerts', which likely serves a related but distinct function in weather data retrieval.

    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 doesn't mention the sibling tool 'get_alerts' or explain scenarios where one might be preferred over the other, leaving the agent without contextual usage cues.

    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. It states what the tool does but provides no information about rate limits, authentication requirements, error conditions, response format, or whether this is a read-only operation. The description doesn't contradict any annotations (since none exist), but it fails to provide essential behavioral context that would help an agent use the tool effectively.

    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 appropriately concise with two sentences that each serve a clear purpose. The first sentence states the tool's core functionality, and the second provides parameter details with examples. There's no unnecessary information, and the structure is logical with purpose first followed by parameter clarification.

    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 has an output schema (which handles return values) and only one parameter with good semantic clarification in the description, the description is reasonably complete for a simple lookup tool. However, the lack of behavioral context (rate limits, auth requirements, error handling) and usage guidance relative to the sibling tool leaves gaps that could hinder effective tool selection and invocation.

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

    Parameters4/5

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

    The description adds significant value beyond the input schema, which has 0% description coverage. While the schema only indicates 'state' is a required string parameter, the description specifies it must be a 'Two-letter US state code (e.g. CA, NY)' and provides concrete examples. This clarifies the expected format and valid values, compensating well 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.

    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: 'Get weather alerts for a US state.' It specifies the verb ('Get'), resource ('weather alerts'), and geographic scope ('US state'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from its sibling tool 'get_forecast' beyond the different resource type (alerts vs forecast).

    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. While it mentions the sibling tool 'get_forecast' exists, there's no explanation of when to choose alerts over forecasts or vice versa. The only contextual information is the geographic limitation to US states, but this doesn't help with tool selection decisions.

    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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