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AmplifyAutomation

Weather MCP Server

by AmplifyAutomation
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Python
Remote

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    get_alerts and get_forecast are clearly distinct in both purpose and input parameters—one targets alerts by state, the other forecasts by coordinates. There is no overlap or ambiguity between the two.

    Naming Consistency5/5

    Both tools follow the same get_ verb_noun pattern, making the naming scheme predictable and consistent. No mixed conventions or vague verbs.

    Tool Count3/5

    With only two tools, the server sits at the thin end of the spectrum. Each tool is useful, but the count feels minimal for a weather service that could reasonably offer current conditions, hourly forecasts, or location search.

    Completeness3/5

    The core alert and forecast capabilities are covered, but obvious weather-domain operations are missing, such as current conditions, location search, or more granular forecast types. Agents may need to work around these gaps.

  • 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?

    There are no annotations, so the description bears the full burden of behavioral disclosure. It only says 'Get weather forecast' and gives no information about safety (read-only vs. side effects), units, time range, caching, or output structure. Such missing details matter for an external forecast API.

    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 short and purpose-first, with a separate Args block for parameters. It is efficiently structured, though the Args section largely duplicates the input schema and could be trimmed without loss.

    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?

    With no output schema and no annotations, the description should explain what forecast data is returned (e.g., temperature, precipitation, time horizon), coordinate constraints, and how this relates to the sibling get_alerts. It does none of these, so an agent lacks essential information for correct invocation and interpretation.

    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 description repeats the parameter names with minimal elaboration ('Latitude of the location'), which adds little beyond the input schema titles 'Latitude' and 'Longitude'. It does not specify units (e.g., decimal degrees), valid ranges, or coordinate format, so the parameter semantics are under-specified.

    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 states a clear verb and resource: 'Get weather forecast for a location.' This distinguishes it from the sibling tool get_alerts, which obviously deals with alerts rather than forecasts, even though it does not explicitly name the sibling.

    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 about when to use get_forecast versus get_alerts or any other alternative. The description gives no contextual hints about use cases, prerequisites, or exclusions.

    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, the description must carry the behavioral disclosure burden, but it only restates the core purpose of retrieving alerts. It does not mention return format, data source, update frequency, or whether this is a safe read-only operation. This is a minimal behavioral statement rather than useful 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?

    The description is compact, front-loaded, and every sentence earns a place: the first line states the action, the second documents the only parameter. There is no filler or redundant information.

    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?

    For a tool with one simple parameter, the description is near-adequate. However, with no output schema and no annotations, it does not describe what the returned alerts contain or how to distinguish this tool from get_forecast when selecting. A small but real completion gap remains.

    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 input schema only defines state as a string, so the description's 'Two-letter US state code (e.g. CA, NY)' adds important format and example context. It fully clarifies the sole parameter, though it does not enumerate all valid state codes or explain invalid input behavior.

    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 opens with a specific verb and object ('Get weather alerts for a US state'), clearly identifying the tool's function. It does not explicitly contrast with sibling get_forecast, but the word 'alerts' already separates it from a forecast tool. Clear, but stops short of explicit sibling differentiation.

    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?

    There is no guidance on when to choose this tool over get_forecast or what conditions call for alerts versus forecast. The only usage-related content is the state parameter, which belongs more to parameter semantics. The agent receives no decision support for tool selection.

    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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