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jikime

Python MCP Korea Weather Service

by jikime

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_forecast retrieves weather forecast data, while get_grid_location provides grid coordinates needed for API calls. There is no overlap in functionality, and an agent can easily tell them apart based on their descriptions.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (get_forecast, get_grid_location). The naming is predictable and readable, with no deviations or mixed conventions.

    Tool Count2/5

    With only two tools, the server feels thin for a weather service domain. While the tools cover forecast retrieval and coordinate lookup, there are likely gaps in functionality (e.g., historical data, alerts, or broader regional coverage) that could limit agent workflows.

    Completeness2/5

    The tool set is severely incomplete for a weather service. It lacks essential operations such as historical weather data, severe weather alerts, multi-day forecasts, or location search beyond grid coordinates. Agents will face dead ends when trying to perform common weather-related tasks.

  • Average 3.6/5 across 2 of 2 tools scored. Lowest: 2.8/5.

    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 is passing
  • This repository is licensed under Apache 2.0.

  • 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. It describes what the tool does (calls an API, provides weather data) and the timeframe (within 6 hours), but lacks critical behavioral information such as rate limits, authentication requirements, error handling, response format details, or whether this is a read-only operation. For a tool that calls an external API with 5 required parameters, this represents significant gaps in behavioral transparency.

    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 reasonably concise with three sentences that each add value. The first sentence establishes the core functionality, the second explains the input basis, and the third details the output content and timeframe. There's no redundant information, and the structure flows logically from purpose to implementation to output details.

    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 (5 required parameters, external API call) and the presence of an output schema, the description provides basic contextual information about what the tool does and what data it returns. However, with no annotations and poor parameter documentation, it lacks sufficient information about behavioral aspects, parameter usage, and differentiation from sibling tools. The output schema existence reduces the need to describe return values, but other gaps remain significant.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With 0% schema description coverage for all 5 parameters, the description provides minimal parameter semantics. It mentions that the tool uses 'region information and grid coordinates' as input, which vaguely corresponds to the city, gu, dong, nx, and ny parameters, but doesn't explain what each parameter represents, their relationships, valid values, or how they should be formatted. The description fails to compensate for the complete lack of schema documentation.

    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: it calls the Korea Meteorological Administration's ultra-short-term forecast API to provide weather forecast information for a specific region. It specifies the data source, timeframe (within 6 hours), and types of weather information included (temperature, precipitation, sky conditions, humidity, wind direction, wind speed). However, it doesn't explicitly differentiate from the sibling tool 'get_grid_location' beyond mentioning grid coordinates as input.

    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 using grid coordinates and region information as input, it doesn't explain when this tool is appropriate compared to the sibling 'get_grid_location' or other potential weather tools. There's no mention of prerequisites, limitations, or specific use cases.

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

  • Behavior3/5

    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 describes the action (retrieves from database), input basis (administrative divisions), and purpose (obtain coordinates for API calls). However, it lacks details on error handling, database limitations, or response format, which are important for a tool with no annotation coverage.

    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 efficiently structured in three sentences: first states the tool's purpose, second explains the input-output mapping, third provides usage context. Each sentence adds essential information without redundancy, making it appropriately concise and front-loaded.

    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 the tool's moderate complexity (3 required parameters, no annotations, but has an output schema), the description is mostly complete. It covers purpose, parameters, and usage context. The output schema likely handles return value documentation, so the description doesn't need to explain outputs. However, it could benefit from more behavioral details like error cases or data freshness.

    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 input schema has 0% description coverage, so the description must compensate. It explicitly explains the meaning of all three parameters: '시/도, 구/군, 동/읍/면 정보' (city/province, district, neighborhood/town/village), clarifying that these are administrative divisions used to search the database. This adds significant value beyond the schema's bare property names.

    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 tool's purpose: '조회합니다' (retrieves) grid coordinates (nx, ny) from a database based on administrative divisions. It specifies the resource (격자 좌표), the source (한국 기상청 API), and distinguishes it from the sibling tool get_forecast by focusing on coordinate lookup rather than weather forecasting.

    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?

    The description provides clear context for when to use this tool: '기상청 API 호출에 필요한 정확한 좌표값을 얻기 위해 필수적으로 사용됩니다' (essential for obtaining accurate coordinates needed for Korea Meteorological Administration API calls). However, it does not explicitly mention when not to use it or name alternatives beyond the implied distinction from get_forecast.

    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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