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veysby

MCP Quickstart Weather Server

by veysby
OverviewSchemaRelated ServersScoreDiscussions
Python

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 alerts for a US state, while get_forecast provides forecasts for a specific latitude/longitude location. 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 the same verb 'get' and descriptive nouns. The naming is predictable and uniform throughout the set.

    Tool Count2/5

    With only 2 tools, the server feels thin for a weather domain. While it covers alerts and forecasts, it lacks other common weather operations like current conditions, historical data, or radar information, making the scope limited.

    Completeness2/5

    The tool surface is significantly incomplete for a weather server. It misses core functionalities such as getting current weather, historical data, or supporting broader geographic queries beyond US states or specific coordinates, which will limit agent effectiveness.

  • 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
    • 3 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

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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. While it indicates this is a read operation ('Get'), it doesn't disclose important behavioral traits like rate limits, authentication requirements, data freshness, error conditions, or what format the forecast returns. For a tool with zero annotation coverage, 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 in the first sentence. The parameter documentation is structured clearly with an 'Args:' section. While efficient, the second sentence could be more polished (e.g., using 'Parameters:' instead of 'Args:').

    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?

    Given the tool's moderate complexity (2 required parameters), no annotations, and no output schema, the description is incomplete. It doesn't explain what the forecast returns (temperature, precipitation, timeframe), error handling, or any limitations. For a weather API tool with zero structured metadata, the description should provide more complete 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?

    The description explicitly lists both parameters (latitude and longitude) with brief explanations, adding meaningful context beyond the 0% schema description coverage. However, it doesn't provide format details (e.g., decimal degrees), valid ranges, or coordinate system information. The description compensates somewhat for the schema gap but doesn't fully document parameter semantics.

    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'). It distinguishes itself from the sibling tool 'get_alerts' by focusing on forecasts rather than alerts. However, it doesn't explicitly differentiate between the two tools in the description text itself.

    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 simply states what the tool does without any context about appropriate use cases, prerequisites, or exclusions. There's no mention of when this tool is preferred over other weather-related tools.

    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 what the tool does but lacks details on traits like rate limits, error handling, authentication needs, or response format. This is a significant gap for a tool with no structured safety hints.

    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 structured 'Args:' section. Every sentence earns its place with no wasted words, making it highly efficient and easy to parse.

    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 (1 parameter, no nested objects) and lack of annotations or output schema, the description is minimally adequate. It explains the purpose and parameter but misses behavioral context and sibling differentiation, leaving gaps that could hinder optimal 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 schema description coverage is 0%, so the description must compensate. It adds crucial semantics by explaining the 'state' parameter as a 'Two-letter US state code' with examples (e.g., CA, NY), which is not evident from the schema alone. This effectively documents the single parameter, though it doesn't cover edge cases.

    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 other alternatives. It mentions the scope ('US state') but offers no context about use cases, exclusions, or prerequisites, leaving the agent to infer usage.

    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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