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microsoft

MCP Weather Server

by microsoft
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TypeScript

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 weather alerts for a state, while get_forecast provides weather forecasts for a location. There is no overlap in functionality, making it easy for an agent to select the correct tool based on the need for alerts versus forecasts.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'get_' prefix and descriptive nouns (alerts, forecast). This uniformity enhances readability and predictability, making the tool set easy to navigate and understand.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for a weather domain, which typically involves more operations like current conditions, historical data, or radar information. This limited set may restrict agent capabilities in handling comprehensive weather-related tasks.

    Completeness2/5

    The tool surface is significantly incomplete for a weather server, lacking essential operations such as getting current conditions, historical weather data, or radar imagery. This creates notable gaps that could lead to agent failures when trying to perform common weather-related workflows.

  • Average 2.9/5 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 is failing

  • This repository is licensed under MIT 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 but offers minimal information. It states what the tool does but doesn't cover aspects like whether it's read-only, requires authentication, has rate limits, or what the output format might be, which are critical for a tool with no output schema.

    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 a single, clear sentence with no wasted words, making it highly concise and front-loaded. It efficiently communicates the core function without unnecessary elaboration.

    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?

    Given the tool's simplicity (one parameter, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, output expectations, and usage context relative to the sibling tool, making it insufficient for full agent understanding despite the straightforward schema.

    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 mentions 'for a state', which aligns with the single parameter 'state' in the input schema. Since schema description coverage is 100% (the schema fully documents the parameter as a two-letter state code), the description adds little beyond what the schema provides, meeting the baseline score.

    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 alerts for a state'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'get_forecast', which likely provides different weather data, so it doesn't reach the highest score.

    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_forecast' or specify scenarios where alerts are preferred over forecasts, leaving usage context implied at best.

    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 offers minimal information. It states the action ('Get weather forecast') but doesn't cover critical aspects like rate limits, authentication needs, data freshness, error handling, or response format. This is inadequate for a tool that likely interacts with external data sources.

    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—a single sentence that directly states the tool's purpose without any fluff or redundancy. It's front-loaded with the essential information, making it efficient and easy to parse, though this brevity contributes to gaps in other dimensions.

    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?

    For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the forecast includes (e.g., temperature, precipitation), the time range covered, or how results are structured. Given the complexity of weather data and lack of structured output, more context is needed for effective use.

    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, with clear documentation for both parameters (latitude and longitude), including valid ranges. The description doesn't add any semantic details beyond what the schema provides, such as examples of locations or coordinate systems. Given the high schema coverage, a baseline score of 3 is appropriate.

    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 distinguish this tool from its sibling 'get_alerts', which might also relate to weather information, leaving room for potential confusion about when to use each.

    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 its sibling 'get_alerts' or any alternatives. It lacks context about specific scenarios, prerequisites, or exclusions, leaving the agent to infer usage based solely on the tool name and basic purpose.

    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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