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elmopps

weather-mcp-server

by elmopps
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 have clearly distinct purposes: one for weather alerts by state, another for forecast by coordinates. No overlap in functionality.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (get_alerts, get_forecast), making the naming predictable and easy to understand.

    Tool Count3/5

    With only two tools, the server feels minimal for a weather domain. While it covers two core functions, 2 tools is on the lower end of reasonable scope.

    Completeness3/5

    The server provides alerts and forecast but lacks current conditions, radar, or marine data, leaving notable gaps for a typical weather information service.

  • Average 3.3/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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

  • Behavior1/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. It only states it gets a forecast, with no mention of data sources, limitations, latency, or side effects. This is insufficient for a data-fetching tool.

    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 very short (two sentences) and front-loads the purpose. However, the 'Args' section is redundant with the input schema, slightly reducing efficiency.

    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 existence of an output schema and no nested objects, the description is minimally adequate. It conveys the basic function but lacks context on usage conditions or what the forecast includes. More details would improve completeness.

    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?

    Schema description coverage is 0%, so the description must add meaning. It merely repeats parameter names and descriptions (e.g., 'Latitude of the location') that are already clear from the schema. No additional semantics like valid ranges, units, or defaults are provided.

    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 'Get weather forecast for a location', using a specific verb and resource. It also distinguishes from the sibling tool 'get_alerts', which would provide 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, such as 'get_alerts'. It does not mention any 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?

    No annotations provided, so description carries full burden. It does not disclose behavioral traits like read-only status, rate limits, authentication requirements, or behavior for invalid state codes. Only states it gets alerts, leaving agent uninformed about important behaviors.

    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 front-loaded with the main purpose. The 'Args:' section is efficiently formatted. It contains no fluff, but could be more concise by integrating the parameter format into the main sentence.

    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 simple tool (1 param, output schema present), the description is adequate but minimal. It does not mention that alerts are only for US states or provide any usage examples. The output schema reduces the need to describe return values, but additional context like geographical scope could improve completeness.

    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?

    Schema description coverage is 0%, so description must compensate. It adds meaning by specifying the exact format 'Two-letter US state code' and provides examples ('CA, NY'), which goes beyond the schema's mere type definition.

    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 uses a specific verb 'Get' and resource 'weather alerts' with clear scope 'for a US state'. It distinguishes from sibling tool 'get_forecast' which presumably provides forecasts instead of 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?

    The description implies usage when alerts are needed for a US state, but provides no explicit guidance on when to use this tool over 'get_forecast' or any exclusions. No alternatives or when-not-to-use are mentioned.

    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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