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aitiwari

Weather MCP Server

by aitiwari
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: 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 confusion about which tool to use for different weather-related queries.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (get_alerts, get_forecast) with identical verb usage and snake_case formatting. This makes the tool set predictable and easy to understand at a glance.

    Tool Count2/5

    With only two tools, this server feels under-scoped for a weather domain. While alerts and forecasts are core functions, there are obvious gaps like current conditions, historical data, or radar imagery that would be expected from a weather service. The tool count is too low for comprehensive weather coverage.

    Completeness2/5

    The tool surface is severely incomplete for a weather server. It lacks fundamental operations like getting current weather conditions, accessing radar or satellite imagery, retrieving historical data, or supporting international locations. Agents will encounter dead ends when trying to perform basic weather queries beyond these two specific functions.

  • Average 3.1/5 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 fails to specify the forecast timeframe (e.g., 5-day, hourly), units (metric/imperial), data freshness, or what the return value contains. 'Get weather forecast' is too generic for an agent to predict the operation's scope or output format.

    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 the purpose stated immediately in the first sentence. The Args section is structured and necessary given the empty schema descriptions. No extraneous text is present, though the parameter descriptions are minimal.

    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 low complexity (2 primitive parameters, no nested objects) and lack of output schema, the description meets minimum viability but has clear gaps. It omits the forecast time range, output data structure, and units—information essential for an agent to utilize the results effectively. With no annotations to clarify behavior, the description should provide more operational 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?

    With 0% schema description coverage, the description must compensate for the lack of parameter documentation. The Args block provides basic semantic mapping ('Latitude of the location'), which is minimally sufficient but tautological. It lacks coordinate format details, valid ranges, or examples that would help an agent construct valid inputs.

    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 states a clear specific verb ('Get') and resource ('weather forecast') with scope ('for a location'). However, it does not explicitly differentiate from the sibling tool 'get_alerts' (forecast vs. alerts), which would help an agent select the correct tool when both weather conditions and alerts are relevant.

    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 the sibling 'get_alerts' or other alternatives. There are no prerequisites, conditions, or exclusion criteria mentioned.

    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 must carry the full burden of behavioral disclosure. While 'Get' implies read-only access, the description omits whether the data is real-time or cached, what types of alerts are returned (severe weather, advisories, etc.), and the response format structure.

    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 optimally concise at two sentences with no wasted words. The purpose is front-loaded in the first sentence, followed by the Args section that precisely documents the single parameter.

    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?

    For a single-parameter tool without output schema or annotations, the description adequately covers the essential input requirements. However, it could improve by mentioning the return value structure (e.g., list of active alerts) and distinguishing its use case from 'get_forecast' to achieve higher 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?

    With 0% schema description coverage (the 'state' property has no description field), the description provides crucial format specification that the schema lacks: it clarifies the two-letter code requirement and provides examples (CA, NY). This significantly aids correct parameter construction.

    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 verb (Get), resource (weather alerts), and scope (US state). However, it does not explicitly differentiate from sibling tool 'get_forecast', which could help clarify when to use alerts versus 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, nor does it mention any prerequisites such as API keys or location validation. No 'when-not' or exclusion criteria are provided.

    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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