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ACAne0320

amap-weather-mcp-server

by ACAne0320
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

  • Disambiguation5/5

    Each tool serves a clearly distinct purpose: real-time current weather, multi-day forecast, and city lookup by keyword. There is no functional overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case (get_current_weather, get_weather_forecast, search_city). The naming is predictable and clear.

    Tool Count5/5

    With exactly three tools covering current weather, forecast, and city search, the server is tightly scoped for its weather information purpose. This count is minimal but sufficient and well within the 3-15 ideal range.

    Completeness5/5

    The server covers the core weather workflow: search for a city, get current conditions, and get a multi-day forecast. There are no obvious gaps for a general weather query service; all essential operations are present.

  • Average 3.2/5 across 3 of 3 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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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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 exist, and the description does not disclose behaviors such as whether multiple cities are returned, ordering, or pagination. The agent lacks insight into what to expect.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

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

    The description is very short and concise, but lacks structure. It mixes Chinese and English informally, and the args section is simplistic. Could be more clearly formatted.

    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?

    For a search tool with no output schema and low schema description coverage, the description is insufficient. It does not explain what data is returned or how results are structured.

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

    Parameters1/5

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

    The description only repeats the parameter name 'keyword' without adding meaning. Schema description coverage is 0%, and the description fails to compensate by explaining the parameter's purpose or format.

    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 'search for matching cities by keyword', which is a specific verb and resource. It distinguishes itself from weather-related sibling tools.

    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. While siblings are weather tools, no explicit usage context or exclusions are provided.

    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 bears full responsibility. It only states the tool 'gets' weather, implying a read operation, but fails to disclose potential behaviors such as rate limits, authentication needs, or error handling. Essential transparency is missing.

    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 concise, with two clear lines for purpose and parameters. The args section is structured. However, the purpose could be more tightly integrated with the parameter description to reduce 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?

    Despite low complexity (single parameter, no nested objects), the description lacks information about the output format (e.g., temperature, conditions) and does not specify any edge cases or behavior when the city is not found. This omission hinders the agent's ability to fully understand the tool's capabilities.

    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?

    With 0% schema description coverage, the description compensates by explaining the 'city' parameter as '中国城市名称' (Chinese city name) and providing examples. This adds useful meaning beyond the schema, though it does not specify format constraints or acceptable input variations.

    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 states '获取指定城市的实时天气' (get real-time weather for specified city), clearly indicating the verb and resource. However, it does not differentiate from the sibling tool 'get_weather_forecast', as 'current' is only implied by the tool name.

    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 provides context that the tool is for current weather of a city, but it does not explicitly advise when to use this tool versus 'get_weather_forecast' or 'search_city'. Usage is implied but not explicitly guided.

    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 must disclose behavioral traits. It states the forecast covers 3-4 days but lacks details on data source, update frequency, or any limitations. The description is too minimal for full transparency.

    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 extremely concise: one line in Chinese stating the purpose, followed by a clear parameter description in English. No wasted words; information is 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 simplicity (one parameter, no output schema, no annotations), the description covers the essential aspects. It could mention that the forecast is not current weather, but overall it is adequate for the complexity level.

    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 has 0% description coverage, but the description adds example values ('北京市', '上海市', '广州市') which clarify the expected format beyond the schema's plain 'string' type. However, it could specify required format (e.g., city name must include '市').

    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 retrieves a weather forecast for a specified city, covering 3-4 days. It uses specific verb ('get') and resource ('weather forecast'), and distinguishes from siblings like 'get_current_weather' (current vs forecast) and 'search_city' (search vs 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 provides an example of city format (e.g., '北京市'), but does not explicitly state when to use this tool versus its siblings 'get_current_weather' or 'search_city'. Usage context is implied but not spelled out.

    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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