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Lil-Code30

nws-mcp-server

by Lil-Code30
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TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.2-0

  • Disambiguation5/5

    The two tools serve clearly distinct purposes: one for weather alerts and one for forecasts. There is no overlap in functionality.

    Naming Consistency3/5

    Both tools follow a verb_noun pattern, but one uses underscores (get_alerts) while the other uses a hyphen (get-forecast). This inconsistency could confuse an agent expecting uniform naming.

    Tool Count2/5

    With only two tools, the server feels too thin for a comprehensive weather service. While it covers alerts and forecasts, it lacks other common NWS operations like current conditions.

    Completeness2/5

    The tool set provides only alerts and forecasts, missing significant NWS operations such as current observations, radar, or marine forecasts. This creates notable dead ends for agents.

  • Average 3.2/5 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 MIT License.

  • This repository includes a README.md file.

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    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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}

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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, the description bears the full burden of behavioral disclosure. It fails to mention data sources, units, time range, update frequency, or any limitations. The description is too vague to inform the agent about operational nuances.

    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 very short (one sentence), but it contains a typo and lacks substantive detail. While it is concise, it sacrifices clarity and completeness.

    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 simplicity of the tool (2 parameters, no output schema) and absence of annotations, the description is inadequate. It does not specify forecast parameters like temperature units or time period, leaving the agent uncertain about the tool's output.

    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% coverage with clear descriptions for latitude and longitude. The description adds no extra semantic value beyond the schema, so 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 'Get weather forcast for a location' clearly indicates the verb (get) and resource (forecast), distinguishing it from the sibling tool 'get_alerts' which likely handles weather alerts. However, the description lacks specificity on forecast type (e.g., temperature, hourly/daily) and contains a typo ('forcast').

    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 the sibling 'get_alerts'. There are no explicit use cases, prerequisites, or exclusions, leaving the agent without context for tool selection.

    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 provided; the description does not disclose any behavioral traits such as authentication requirements, rate limits, or output characteristics. The minimal statement fails to inform an agent about side effects or limitations.

    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, focused sentence of six words. It is front-loaded and contains no fluff, earning its place efficiently.

    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 simplicity of the tool (one parameter, no annotations, no output schema), the description covers the basic purpose but lacks context on behavior or output structure. Adequate but minimal.

    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 provides 100% coverage for the 'state' parameter with a clear description and example. The description does not add additional meaning beyond the schema, so baseline score applies.

    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 the verb 'Get' and the resource 'weather alerts' with scope 'for a state'. It effectively distinguishes from the sibling tool 'get-forecast' which implies forecasts vs 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?

    No explicit guidance on when to use this tool versus the sibling 'get-forecast'. The description implies the purpose but does not outline conditions or alternatives beyond the tool's name.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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