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atan2016

Weather MCP Server

by atan2016
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools, get_alerts and get_forecast, serve clearly distinct purposes: one for alerts by state, the other for forecast by coordinates. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow the same `get_` prefix with a noun, forming a consistent and predictable verb_noun pattern.

    Tool Count3/5

    With only two tools, the server feels somewhat thin for a weather domain, but the selection is focused and each tool serves a meaningful purpose. It is on the borderline of being too few.

    Completeness4/5

    The server covers alerts and forecasts, two common weather needs, but lacks current conditions or other weather data. This is a minor gap, as the core functionality is present.

  • Average 3.6/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 1 commit 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

  • Behavior2/5

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

    There are no annotations, so the description carries the full burden of behavioral disclosure. It only mentions the action of retrieving a forecast without detailing behaviors such as units (metric/imperial), forecast period (current, 7-day, etc.), data source, or potential error 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 extremely concise and the primary sentence is front-loaded. However, the Args block is redundant with the schema, repeating property names and basic meanings that are already present in the schema titles, which slightly wastes space.

    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 the simplicity of the tool, the description lacks essential context: it doesn't specify what the forecast contains (temperature, precipitation, wind), the units used, or the forecast duration. The output schema is present but not described, so an agent must guess what to expect from the return value.

    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 input schema has zero description coverage, and the description's Args section only restates the schema property names ('Latitude' and 'Longitude') with trivial words like 'of the location'. No additional semantic details are provided, such as valid ranges, units, or coordinate format.

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

    Purpose5/5

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

    The description clearly states 'Get weather forecast for a location' with a specific verb and resource. This distinguishes it from the sibling tool 'get_alerts', which presumably handles alerts rather than forecasts.

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

    Usage Guidelines3/5

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

    The implied usage is clear: use this tool when a weather forecast is needed. However, there is no explicit guidance about when to use this over 'get_alerts' or any exclusions, leaving the distinction to be inferred from the name alone.

    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?

    No annotations are provided, so the description carries the full burden for behavioral disclosure. It does not mention potential side effects, authentication requirements, rate limits, or any limitations of the alerts data. The only transparency is that it is a 'get' operation, which is implicitly read-only, but this is not explicitly stated.

    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?

    The description is extremely concise, consisting of one sentence for the purpose and a brief argument explanation. It is front-loaded with the main action and contains no unnecessary fluff, earning its place every word.

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

    Completeness4/5

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

    For a simple tool with one parameter and an output schema, the description provides the core purpose and parameter semantics. It does not cover edge cases like invalid states or data sources, but given the low complexity and existing output schema, it is reasonably complete. The lack of usage guidance is a minor gap that is already penalized under usage_guidelines.

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

    Parameters5/5

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

    The input schema only defines 'state' as a string with no description, giving 0% coverage. The description compensates by specifying a two-letter US state code and providing examples (CA, NY), which is essential for correct invocation. This fully clarifies the expected format.

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

    Purpose5/5

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

    The description clearly states 'Get weather alerts for a US state' using a specific verb and resource, and the scope is explicit. This distinguishes it from the sibling get_forecast, which presumably returns forecasts rather than alerts.

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

    Usage Guidelines3/5

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

    The description implies that the tool is used for weather alerts, but it does not explicitly differentiate when to use this vs. get_forecast or provide any exclusions. No alternatives are mentioned, leaving usage context merely implied.

    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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