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santosh-ksharma

Weather MCP Server

by santosh-ksharma
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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 alerts for US states, while get_forecast provides forecasts for geographic coordinates. There is no overlap in functionality or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun 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, the server feels thin for a weather domain. While alerts and forecasts are core features, obvious gaps like current conditions, historical data, or radar imagery suggest the tool count is too low for comprehensive weather coverage.

    Completeness2/5

    The tool surface is severely incomplete for weather services. It lacks essential operations such as getting current conditions, historical weather data, radar maps, or air quality information. Agents will face significant limitations when trying to perform common 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
    • No commit activity data available
    • 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 provided, the description carries the full burden of behavioral disclosure. It only states the basic action ('Get weather forecast') without mentioning any behavioral traits such as rate limits, authentication needs, data freshness, error handling, or what the forecast includes (e.g., temperature, precipitation). This leaves significant gaps in understanding how the tool 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 and front-loaded, with the core purpose stated first in a clear sentence. The parameter list is concise and directly relevant. While efficient, it could be slightly improved by integrating parameter details more seamlessly, but it avoids unnecessary verbosity.

    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) and the presence of an output schema, the description is minimally adequate. It covers the basic purpose and parameters but lacks behavioral context and usage guidelines. The output schema likely handles return values, so the description doesn't need to explain those, but overall completeness is limited.

    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 lists the parameters (latitude and longitude) and their purpose ('of the location'), which adds some meaning beyond the input schema's basic titles and types. However, with 0% schema description coverage, it doesn't fully compensate by providing details like valid ranges, units, or examples, keeping it at the baseline for minimal parameter context.

    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 with a specific verb ('Get') and resource ('weather forecast for a location'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'get_alerts', which might provide related weather information, 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 like 'get_alerts'. It states what the tool does but offers no context about appropriate use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves data ('Get'), implying a read-only operation, but doesn't cover aspects like rate limits, error handling, authentication needs, or what the output contains. This leaves significant gaps for a tool with no annotation support.

    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, starting with the core purpose followed by parameter details in a structured 'Args' section. It avoids unnecessary fluff, though the formatting could be slightly more polished for optimal readability.

    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 low complexity (one parameter) and the presence of an output schema (which handles return values), the description is moderately complete. However, with no annotations and minimal behavioral context, it doesn't fully equip the agent for reliable use, especially regarding error cases or limitations.

    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 description adds meaningful context beyond the input schema, which has 0% description coverage. It explains that the 'state' parameter is a 'Two-letter US state code' with examples (e.g., CA, NY), clarifying the expected format. Since there's only one parameter, this compensates well for the schema's lack of details.

    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 with a specific verb ('Get') and resource ('weather alerts for a US state'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'get_forecast', which likely provides different weather data, 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 its sibling 'get_forecast' or any alternatives. It mentions the scope ('US state') but lacks explicit instructions on usage context, exclusions, or prerequisites, leaving the agent to infer based on tool names alone.

    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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