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shreyyzsh

Claude-NWS Protocol Bridge

by shreyyzsh
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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 weather alerts for a US state, while get_forecast provides weather forecasts for a specific geographic location. There is no overlap in functionality or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (get_alerts and get_forecast), using the same verb 'get' followed by a descriptive noun. This makes the tool set predictable and easy to understand.

    Tool Count2/5

    With only two tools, the server feels thin for a weather service domain. While the tools cover alerts and forecasts, there are likely missing operations such as current conditions, radar data, or historical data that would make the set more complete for typical weather-related tasks.

    Completeness2/5

    For a weather service, the tool set is significantly incomplete. It lacks core functionalities like getting current weather conditions, radar or satellite imagery, historical data, or air quality information. This limited coverage will likely cause agent failures when trying to perform comprehensive weather-related tasks.

  • 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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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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states what the tool does without mentioning any behavioral traits such as rate limits, authentication needs, error handling, data freshness, or what the response format looks like. For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

    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 sized with two sentences: one stating the purpose and another listing parameters. It's front-loaded with the main functionality. However, the 'Args' section could be integrated more smoothly, and there's some redundancy in repeating parameter names without added value.

    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 complexity (a weather forecasting tool with 2 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the forecast includes (e.g., temperature, precipitation), time frames, data sources, or error cases. This makes it inadequate for an agent to use the tool effectively without additional context.

    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, which adds meaning beyond the input schema (which has 0% description coverage). However, it doesn't provide additional details like valid ranges, units, or examples. With low schema coverage, the description compensates somewhat but not fully.

    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: 'Getting weather forecast for a location.' It specifies the verb ('Getting') and resource ('weather forecast'), making it easy to understand what the tool does. However, it doesn't distinguish from its sibling tool 'get_alerts', which appears to be related but serves a different function (likely getting weather 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 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 the difference between getting forecasts and alerts. There's no context about prerequisites, limitations, or appropriate use cases beyond the basic functionality.

    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 of behavioral disclosure. It describes a read operation ('Getting'), implying it's non-destructive, but lacks details on permissions, rate limits, error handling, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

    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 sized and front-loaded, with the purpose stated first and parameter details following. It uses two sentences efficiently, with no redundant information. However, the formatting could be slightly improved for better readability.

    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 lack of annotations and output schema, the description is incomplete. It covers the basic purpose and parameter semantics but fails to address behavioral aspects like response format, error conditions, or usage context. For a tool with no structured data support, more comprehensive guidance is needed.

    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 schema description coverage is 0%, so the description must compensate. It adds meaning by specifying that 'state' is a 'two-letter us state code (e.g CA, NY)', which clarifies the parameter's format and provides examples. This is valuable beyond the basic schema, but it doesn't cover all potential nuances (e.g., validation rules).

    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: 'Getting weather alerts for a US State.' It specifies the verb ('Getting'), resource ('weather alerts'), and scope ('for a US State'), which is unambiguous. However, it doesn't explicitly differentiate from its sibling 'get_forecast' (which likely provides forecasts rather than alerts), 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. There is no mention of its sibling 'get_forecast' or any other tools, nor does it specify prerequisites, exclusions, or contextual cues for usage. It merely states what the tool does without indicating appropriate scenarios.

    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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