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deepcodes7

MCP Weather Server

by deepcodes7
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

  • Disambiguation4/5

    The two tools have distinct purposes: get_weather for current conditions and get_forecast for multi-day predictions. However, the names could cause minor confusion as 'weather' and 'forecast' are semantically related, but the descriptions clearly differentiate them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'get_' prefix and snake_case naming. This makes them predictable and easy to understand at a glance.

    Tool Count2/5

    With only two tools, the server feels under-scoped for a weather domain. It lacks operations like historical data, alerts, or location-based searches, which are common in weather APIs and would enhance agent capabilities.

    Completeness2/5

    The toolset is severely incomplete for a weather server. It covers current and forecast data but misses essential operations like historical weather, severe alerts, air quality, or multi-location queries, leaving significant gaps for agent workflows.

  • Average 3.2/5 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 states the tool returns 'A formatted multi-day forecast summary,' which hints at output format but lacks details on error handling, rate limits, authentication needs, or data sources. For a tool with no annotations, 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.

    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' and 'Returns' section. Every sentence adds value without redundancy, making it efficient and easy to parse. The formatting enhances clarity without 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 parameters, no annotations, but with an output schema), the description is minimally adequate. The output schema exists, so the description needn't detail return values, but it lacks context on sibling tool differentiation and behavioral traits. It meets basic needs but has clear gaps in usage guidance and transparency.

    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 semantics beyond the input schema, which has 0% description coverage. It explains that 'city' is the 'Name of the city' and 'days' is the 'Number of days to forecast (1-5, default 3),' including range and default value. This compensates well for the schema's lack of descriptions, though it doesn't cover edge cases like city name formatting.

    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 multi-day weather forecast for a city.' It specifies the verb ('Get'), resource ('weather forecast'), and scope ('multi-day' and 'for a city'). However, it doesn't explicitly differentiate from the sibling tool 'get_weather', which likely provides current weather rather than a forecast.

    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_weather' or any alternatives. It mentions the basic functionality 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.

  • 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 states the tool returns 'current weather conditions' as a 'formatted string,' which gives some output context. However, it lacks details on error handling, rate limits, authentication needs, data sources, or whether it's a read-only operation, leaving 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is well-structured and front-loaded, starting with the core purpose. The 'Args' and 'Returns' sections are clear and efficient, with no wasted words. Every sentence adds value, 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 (1 parameter) and the presence of an output schema (which handles return values), the description is minimally adequate. It covers the basic purpose and parameter meaning, but with no annotations and incomplete behavioral details, it leaves gaps in transparency and guidelines, making it just viable but not fully helpful.

    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 meaningful semantics by explaining the 'city' parameter as 'Name of the city' with examples ('Toronto', 'London', 'Tokyo'), which clarifies usage beyond the bare schema. However, it doesn't address edge cases like city name ambiguities or formatting requirements, preventing a perfect score.

    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 the current weather for a city.' It specifies the verb ('Get') and resource ('current weather'), making the action clear. However, it doesn't explicitly differentiate from its sibling 'get_forecast' (which presumably provides future weather predictions), 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. It doesn't mention the sibling tool 'get_forecast' or clarify that this is for current conditions only. There's no context about prerequisites, limitations, or when not to use it, leaving the agent without usage direction.

    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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