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agentventure

Weather MCP Server

by agentventure
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Python
Remote

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 about which tool to use for each task.

    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 and uniform across the tool set.

    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 make the tool set more complete and useful.

    Completeness2/5

    The tool set is severely incomplete for a weather server. It lacks basic operations like getting current weather conditions, historical data, or radar information. The two tools provided create dead ends for common agent workflows that require more comprehensive weather data.

  • 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?

    With no annotations provided, the description carries full burden but offers minimal behavioral context. It doesn't disclose whether this is a read-only operation, what data format is returned, potential rate limits, authentication requirements, or error conditions. The description is functionally basic.

    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 concise with two sentences and an Args section. The purpose is front-loaded, and the parameter explanations are efficiently presented. No unnecessary information is included.

    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?

    For a tool with no annotations, no output schema, and 2 required parameters, the description is incomplete. It doesn't explain what the forecast returns (temperature, precipitation, timeframe), error handling, data sources, or any behavioral characteristics. The basic purpose and parameter explanation are insufficient for full understanding.

    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 documentation. The description adds the Args section explaining that latitude and longitude represent the location coordinates, which provides basic semantic meaning. However, it doesn't specify coordinate ranges, units, or format expectations beyond what's obvious from parameter names.

    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 'Get weather forecast for a location', specifying the verb 'Get' and resource 'weather forecast'. It distinguishes from the sibling 'get_alerts' by focusing on forecasts rather than alerts, though it doesn't explicitly mention this distinction.

    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 the sibling 'get_alerts' or other alternatives. The description only states what the tool does without any context about appropriate usage scenarios 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 are provided, so the description carries the full burden of behavioral disclosure. It mentions that alerts are for a 'US state,' implying geographic limitations, but doesn't describe other behaviors such as error handling, rate limits, authentication needs, or what the return format looks like (e.g., list of alerts, timestamps, severity). For a tool with no annotations, this leaves significant gaps.

    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 parameter explanation. Every sentence adds value without redundancy, and the structure is clear and efficient, making it easy for an agent to parse quickly.

    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, no annotations, no output schema), the description is minimally adequate. It covers the basic purpose and parameter format but lacks details on behavioral traits, error handling, and output structure. Without annotations or an output schema, more context would improve completeness for reliable agent use.

    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 for the single parameter 'state' by specifying it as a 'Two-letter US state code (e.g. CA, NY),' which clarifies the expected format beyond the schema's basic 'string' type. With 0% schema description coverage and only one parameter, this adequately compensates, though it doesn't cover edge cases like invalid codes.

    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: 'Get weather alerts for a US state.' It specifies the verb ('Get') and resource ('weather alerts') with geographic scope ('US state'). However, it doesn't explicitly differentiate from its sibling tool 'get_forecast' beyond the resource type, which prevents a perfect 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_forecast.' It states what the tool does but offers no context about appropriate use cases, prerequisites, or exclusions. The agent must infer usage from the tool name and description 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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