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tjguitar2025

Context Weather

by tjguitar2025
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 are clearly distinct: get_alerts targets state-level weather alerts, while get_forecast targets point-based forecast. No overlap in purpose or arguments.

    Naming Consistency5/5

    Both tools follow a consistent get_verb_noun pattern (get_alerts, get_forecast), which is predictable and clear.

    Tool Count3/5

    With only 2 tools, the server feels minimal. While it serves a focused purpose, a typical weather API would offer more operations, making the count borderline.

    Completeness4/5

    The server covers alerts and forecast, but lacks current conditions or radar data. The gaps are minor given the server's 'context' focus.

  • Average 3.5/5 across 2 of 2 tools scored. Lowest: 2.7/5.

    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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  • 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, the description carries full responsibility for behavioral disclosure. It only states the basic action without revealing traits like data freshness, units, rate limits, or whether the operation is read-only. The agent cannot infer important constraints or side effects.

    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 concise with two short sentences, avoiding fluff. However, the structure is a simple paragraph without front-loading the most critical details. It is efficient but could be better organized to highlight key information first.

    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 (2 params, no output schema), the description omits essential details such as the forecast timeframe, units, and response format. The absence of output schema increases the need for description completeness, which is not met. The agent is left guessing what data will be returned.

    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 adds minimal value beyond the schema's property titles. It restates 'latitude of the location' and 'longitude of the location', which are already obvious from the parameter names. No ranges, formats, or semantics are provided, leaving the agent underinformed despite 0% schema description coverage.

    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 retrieves a weather forecast for a location using latitude/longitude. The verb is explicit and the resource is defined. However, it does not explicitly differentiate from sibling tool 'get_alerts', though the context implies they serve different data types.

    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 alternatives (e.g., get_alerts). There are no explicit usage contexts, prerequisites, or exclusions, leaving the agent without direction on selecting this tool.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

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

    No annotations are provided. Description implies a read operation but does not disclose any behavioral traits like rate limits, authorization requirements, or side effects. Adequate for a simple query tool.

    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?

    Extremely concise with no wasted words. The description is front-loaded with the purpose, followed by parameter details in a clear list format.

    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 no output schema, the description provides sufficient context. It could mention that it returns alerts or is read-only, but it is largely complete.

    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?

    Input schema has 0% description coverage, but the description adds meaning to the state parameter: 'two-letter us state code (e.g CA, NY)', which compensates well.

    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?

    Description explicitly states 'Getting weather alerts for a US State', which is a clear verb+resource combination. It distinguishes from sibling tool get_forecast by focusing on alerts.

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

    Usage Guidelines4/5

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

    Description indicates when to use: for weather alerts of a US state. It does not explicitly state when not to use or mention alternatives, but context is clear.

    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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