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shtansky-bikeleasing

Weather MCP Server

by shtansky-bikeleasing
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JavaScript
Hybrid

Server Quality Checklist

67%
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_weather retrieves weather data, while send_notification handles macOS notifications. There is no overlap in functionality, making it easy for an agent to choose the right tool for each task.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (get_weather and send_notification). The verbs are descriptive and appropriate for their actions, and there are no deviations in style or convention.

    Tool Count2/5

    With only two tools, the server feels thin for a weather domain. It lacks essential operations like forecasting, historical data, or multi-location queries, which are typical for weather services. The scope is too limited for practical agent use.

    Completeness2/5

    The tool surface is severely incomplete for a weather server. It only provides current weather retrieval and notification sending, missing core features like forecasts, alerts, or location-based searches. This will likely cause agent failures when handling common weather-related requests.

  • 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
    • 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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'Get current weather' but doesn't specify aspects like rate limits, error handling, data sources, or whether it requires authentication. For a tool with no annotations, this is a significant gap in transparency about how it behaves.

    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 a single, efficient sentence that is front-loaded with the core purpose. There is no wasted language or unnecessary elaboration, making it easy to parse quickly. It earns its place by clearly stating the tool's function without redundancy.

    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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns, potential errors, or behavioral traits. For a tool with no structured data to rely on, the description should provide more context to help the agent use it effectively, but it falls short.

    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 input schema has 100% description coverage, with the 'city' parameter fully documented. The description adds no additional meaning beyond the schema, such as format examples or constraints. According to the rules, when schema coverage is high (>80%), the baseline score is 3, as the schema does the heavy lifting.

    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 verb ('Get') and resource ('current weather for a city'), making the purpose immediately understandable. However, it doesn't differentiate from the sibling tool 'send_notification', which is unrelated, so this doesn't impact the score negatively. The description avoids tautology by specifying what is being retrieved.

    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, such as other weather-related tools that might exist in a broader context. It only states what the tool does, without mentioning prerequisites, limitations, or scenarios for use. This leaves the agent with minimal context for decision-making.

    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 full burden for behavioral disclosure. It states the tool sends notifications but doesn't mention important behavioral aspects like whether this requires specific macOS permissions, if notifications are persistent or transient, what happens if the system is in Do Not Disturb mode, or whether there are rate limits. The description provides basic functionality but lacks operational 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 a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a simple notification tool and front-loads the essential information about what the tool does.

    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?

    For a simple 2-parameter tool with no output schema and no annotations, the description provides basic functionality but lacks important context. It doesn't explain what happens after sending (success/failure indicators), doesn't connect to the sibling weather tool, and doesn't address macOS-specific considerations. While adequate for the simplest use case, there are clear gaps in completeness.

    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?

    With 100% schema description coverage, the input schema already fully documents both parameters (title and message). The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain what constitutes appropriate weather data formatting, character limits, or how the parameters relate to the weather context mentioned. The baseline 3 is appropriate when the schema does the heavy lifting.

    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 action ('send') and resource ('macOS notification with provided weather data'), making the tool's purpose immediately understandable. However, it doesn't explicitly differentiate from the sibling 'get_weather' tool, which appears to be a data retrieval function rather than a notification delivery mechanism.

    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. While it mentions 'weather data' which might imply usage after obtaining weather information, there's no explicit connection to the sibling 'get_weather' tool or any other alternatives. No context about appropriate scenarios or exclusions is provided.

    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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