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damerakd

MCP Weather Server

by damerakd
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
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_current_weather retrieves current conditions, while get_daily_forecast provides future predictions. There is no overlap in functionality, and an agent can easily differentiate between immediate weather data and multi-day forecasts.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'get_' prefix and descriptive names (current_weather, daily_forecast). The naming is uniform and predictable across the toolset, making it easy for agents to understand and use them.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for a weather domain. A typical weather API would include more operations such as historical data, alerts, or hourly forecasts. This minimal set limits agent capabilities and may require workarounds for common weather-related tasks.

    Completeness2/5

    The toolset is severely incomplete for a weather server. It lacks essential operations like historical weather data, severe weather alerts, air quality information, and hourly forecasts. Agents will face significant gaps when trying to perform comprehensive weather analysis or respond to diverse user queries.

  • Average 3.3/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 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?

    With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a 'Get' operation (implying read-only) and describes the return format, but doesn't mention authentication needs, rate limits, error conditions, or whether the forecast data is cached/live. For a weather API tool with zero annotation coverage, 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 efficiently structured with a clear purpose statement followed by Args and Returns sections. Each sentence adds value, though the 'Returns' section could be slightly more detailed given the output schema exists. Overall, it's appropriately sized and front-loaded.

    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 (3 parameters, no annotations, but with output schema), the description covers the basic purpose and parameters adequately. However, it lacks important context about when to use versus the sibling tool, behavioral constraints, and error handling. The existence of an output schema reduces the need to explain return values, but other gaps remain.

    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 provides meaningful semantic context for all three parameters beyond the schema's 0% coverage. It explains that 'city' is a city name with an example, 'country' is an optional filter with format examples, and 'days' specifies the forecast range with constraints (1-7). This compensates well for the schema's lack of descriptions.

    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 a simple daily weather forecast for the next N days for a city.' It specifies the verb ('Get'), resource ('daily weather forecast'), and scope ('for a city'), though it doesn't explicitly differentiate from its sibling tool 'get_current_weather' beyond implying this is for forecasts rather than current conditions.

    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_current_weather' or any alternatives. It mentions the tool's basic function but lacks explicit when/when-not instructions or prerequisite context, leaving usage decisions to inference.

    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. While it mentions the tool 'gets' data (implying a read operation), it doesn't address important behavioral aspects like error handling, rate limits, authentication requirements, data freshness, or whether it's a real-time or cached service. The description is minimal and lacks behavioral context.

    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 perfectly structured and concise. It begins with a clear purpose statement, then has well-organized sections for Args and Returns with bullet-point style explanations. Every sentence adds value, and there's no redundant or unnecessary information.

    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 that there's an output schema (which handles return values), the description provides good context for a simple read operation. It covers the purpose and parameters well. However, for a tool with no annotations, it could benefit from more behavioral information (like error cases or performance characteristics) to be fully 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 description provides excellent parameter semantics beyond the input schema. The schema has 0% description coverage and only shows parameter names and types. The description adds meaningful context: 'city: City name, e.g. "Berlin"' and 'country: Optional country filter, e.g. "DE" or "Germany".' This includes examples, clarifies that country is optional, and explains what the parameters represent.

    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 current weather conditions for a city.' It specifies the verb ('Get') and resource ('current weather conditions for a city'), but doesn't explicitly differentiate it from its sibling 'get_daily_forecast' beyond the 'current' vs 'daily' distinction. This makes it clear but not fully sibling-differentiated.

    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_daily_forecast' or any other alternatives. It simply states what the tool does without context about when it's appropriate or when other tools might be better suited.

    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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