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MadhurToshniwal

OpenWeather MCP Server

by MadhurToshniwal
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TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation4/5

    Most tools target distinct data (current, forecast, air pollution), but both get_current_weather and get_weather_by_coordinates return current conditions, differing only by input. Descriptions clarify the difference, so confusion is low but possible.

    Naming Consistency5/5

    All tools follow a consistent 'get_<something>' pattern with snake_case. The naming is predictable and easily understood.

    Tool Count4/5

    4 tools is a reasonable count for a weather server covering current, forecast, and air quality. It feels slightly light but not inadequate.

    Completeness4/5

    The set covers essential weather queries (current, forecast, air pollution) but lacks alerts, historical data, or UV index. Minor gaps, but core functionality is present.

  • Average 3.6/5 across 4 of 4 tools scored. Lowest: 2.9/5.

    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. It only states the purpose but does not disclose any behavioral traits such as data sources, update frequency, or whether it is read-only (though inferred from name).

    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 a single, direct sentence with no wasted words. It is concise but could be slightly more informative.

    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 simplicity (2 parameters, no output schema), the description is adequate but minimal. It does not explain what the output contains (e.g., AQI, pollutants).

    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 coverage is 100%, so parameters are already described. The description adds no additional meaning beyond 'specific coordinates'.

    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 (get) and resource (air quality data), and specifies the scope (specific coordinates). However, it does not differentiate from sibling tools like get_current_weather, which may also take coordinates.

    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 on when to use this tool versus alternatives like get_weather_by_coordinates or get_current_weather. No prerequisites or limitations are mentioned.

    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, and the description adds no behavioral context beyond the purpose, missing details on error handling, rate limits, or data freshness.

    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?

    Single sentence is front-loaded and concise, though it could be slightly expanded to be more informative without losing brevity.

    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?

    No output schema exists, and the description does not explain what data is returned (e.g., temperature, humidity), making it incomplete for agent 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?

    Input schema coverage is 100%, with clear parameter descriptions; the description adds no additional semantic detail beyond what the schema provides.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states it gets current weather using geographic coordinates, distinguishing it from siblings like get_air_pollution or get_forecast.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies use when coordinates are available, but lacks explicit guidance on when not to use it or alternatives for city-based queries.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Without annotations, the description discloses the forecast duration (5-day) and interval (3-hour), which is key behavioral context. However, it omits details like data source, error handling, or whether historical data is included.

    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, focused sentence with no redundant words. It efficiently conveys the tool's core functionality.

    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 no output schema, the description provides essential structure (5-day, 3-hour intervals). However, it could be more complete by mentioning typical response fields (e.g., temperature, humidity). Still adequate for a simple tool.

    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?

    Both parameters (city, country_code) are described in the schema with 100% coverage. The description adds no new semantic meaning beyond 'for a specific city', which restates the city parameter. Baseline 3 applies.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the verb 'Get', the resource 'weather forecast', and specifies the time range (5-day) and interval (3-hour) for a specific city, effectively distinguishing it from sibling tools like get_current_weather.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    While the description implies use for city-based forecasts, it lacks explicit guidance on when to use this tool over alternatives (e.g., get_weather_by_coordinates) and does not mention any exclusions or prerequisites.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Lists return values (temperature, humidity, etc.) without annotations. Lacks side-effect info but appropriate for a harmless read operation.

    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?

    Single sentence, front-loaded with purpose, no redundant words.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a simple read tool with no output schema, description sufficiently conveys behavior and expected inputs.

    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 covers both parameters fully (100%). Description adds no extra meaning beyond restating 'city'.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    Clearly states 'current weather conditions' for a 'specific city', distinguishing from siblings like 'get_forecast' or 'get_weather_by_coordinates'.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Implied usage: when current conditions for a city are needed. No explicit when-not or alternatives, but sufficient for simple tool.

    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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