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rsys-vtiwari

Weather MCP Server

by rsys-vtiwari
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
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 US states, while get_forecast provides weather forecasts for geographic coordinates. There is no overlap in functionality or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (get_alerts, get_forecast) with identical verb usage and snake_case formatting. The naming is perfectly predictable across the tool set.

    Tool Count2/5

    With only 2 tools for a weather server, the surface feels thin and incomplete for the domain. A weather service typically requires more operations (e.g., current conditions, historical data, multiple forecast types) to be useful for agents.

    Completeness2/5

    There are significant gaps in coverage for a weather domain. Missing essential operations like current weather conditions, historical data, air quality, or radar information. The two tools alone do not provide a complete weather service surface.

  • Average 3.5/5 across 2 of 2 tools scored. Lowest: 2.9/5.

    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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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 states the tool gets a forecast but doesn't describe what the forecast includes (e.g., temperature, precipitation), time range, data source, rate limits, or error handling. For a tool with no annotations, this leaves significant behavioral gaps.

    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 a clear purpose statement and parameter list. It's front-loaded with the main function. However, the parameter section could be more integrated rather than a separate 'Args:' block, and some redundancy exists (e.g., repeating 'location').

    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 tool's moderate complexity (2 required parameters), no annotations, and an output schema exists (which should cover return values), the description is minimally complete. It states what the tool does and lists parameters, but lacks behavioral details and usage guidance, making it adequate but with clear gaps.

    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?

    Schema description coverage is 0%, so the schema provides no parameter descriptions. The description adds minimal semantics by listing parameters with brief labels ('Latitude of the location', 'Longitude of the location'), but doesn't explain format (e.g., decimal degrees), valid ranges, or examples. It compensates slightly but not fully for the coverage gap.

    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 'Get weather forecast for a location' which specifies the verb ('Get'), resource ('weather forecast'), and scope ('for a location'). However, it doesn't explicitly differentiate from the sibling tool 'get_alerts', which might also be weather-related. The purpose is clear but lacks sibling differentiation.

    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 like 'get_alerts'. It doesn't mention prerequisites, constraints, or any context for choosing between tools. The only implied usage is needing a location's coordinates, but this is covered by parameters.

    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. It mentions what the tool does but lacks behavioral details such as rate limits, authentication requirements, error handling, or what the output contains. The description is minimal and does not compensate for the absence of annotations.

    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 front-loaded with the core purpose in the first sentence, followed by a concise 'Args' section that efficiently explains the parameter. Every sentence earns its place with no wasted words, making it highly readable and structured.

    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?

    Given the tool has an output schema, the description does not need to explain return values. It covers the purpose and parameter semantics well, but lacks behavioral context (e.g., permissions, limits) which is a gap since no annotations are provided. However, for a simple read operation, it is mostly complete.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    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 fully compensate. It provides clear semantics for the single parameter 'state', specifying it as a 'Two-letter US state code' with examples (e.g., CA, NY). This adds essential meaning beyond the basic schema, fully documenting the parameter.

    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 specific action ('Get weather alerts') and resource ('for a US state'), distinguishing it from the sibling tool 'get_forecast' which presumably provides weather forecasts rather than alerts. The verb+resource combination is precise and unambiguous.

    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?

    The description implicitly suggests usage when weather alerts are needed for a US state, but does not explicitly state when to use this tool versus the sibling 'get_forecast' or provide any exclusions. The context is clear but lacks explicit alternative guidance.

    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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