Japan Weather & Alerts MCP
Server Details
Official Japanese weather forecasts, warnings, and earthquake data from the Japan Meteorological Agency (JMA). No API key needed for basic use.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.8/5 across 3 of 3 tools scored.
Each tool has a distinct purpose: earthquakes, weather forecast, and weather warnings. There is no overlap in functionality, making it easy for an agent to select the correct tool.
All tools follow a consistent 'get_<noun>' pattern in snake_case, with clear and predictable names: get_recent_earthquakes, get_weather_forecast, get_weather_warnings.
With only 3 tools, the server is well-scoped for a weather and alerts domain. Each tool covers an essential aspect, and nothing feels extraneous or missing.
The three tools cover core functionalities for weather and alerts: recent earthquakes, forecasts, and warnings. Minor gaps like typhoon or marine weather data exist but are not critical for the stated purpose.
Available Tools
3 toolsget_recent_earthquakes最近の地震情報を取得AInspect
日本で最近観測された地震の一覧（発生時刻・震源地・マグニチュード・最大震度）を気象庁公式データから取得する。
| Name | Required | Description | Default |
|---|---|---|---|
| max_results | No | 最大件数（1〜20） |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full behavioral disclosure burden. It states the data source but does not mention side effects (none expected), rate limits, data freshness (what 'recent' means), or whether the query is real-time or cached.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The entire description is one concise sentence that front-loads the key information: what is retrieved, including attributes and data source. Every component earns its place with no extraneous text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool with one parameter and no output schema, the description adequately lists the attributes returned. However, it lacks definition of the time window for 'recent' earthquakes, which would help expectations. The data source mention adds credibility.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (the single parameter 'max_results' has a description in the schema). The tool description does not add any information about the parameter beyond what the schema provides. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the verb (取得する), resource (日本で最近観測された地震の一覧), and key attributes (発生時刻, 震源地, マグニチュード, 最大震度). It also specifies the data source (気象庁公式データ). This distinguishes it from sibling tools focused on weather.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. While the siblings are weather-related, the description does not state when to choose earthquakes over weather forecasts or warnings. The usage is implied by the tool's name and content.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_weather_forecast日本の天気予報を取得AInspect
気象庁公式データから指定地域の天気予報（今日・明日・明後日＋週間）を取得する。地域は都道府県名で指定（例: 東京都、大阪府、北海道は「石狩・空知・後志地方」等の予報区名）。
| Name | Required | Description | Default |
|---|---|---|---|
| area | Yes | 都道府県・予報区名（例: 東京都 / 大阪府 / 沖縄本島地方） |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It mentions data source (JMA official) and days covered, but does not disclose update frequency, caching, rate limits, or read-only nature. Basic transparency but lacking depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with main purpose and key details. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description does not specify return format or structure. It mentions days covered but omits response details. Adequate for a simple tool but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a description for 'area'. The tool description adds value by providing concrete examples (e.g., Hokkaido forecast district names), making the parameter clearer than schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool retrieves weather forecasts for specific days (today, tomorrow, day after tomorrow, weekly) from official JMA data, with area specified by prefecture or forecast district. This distinguishes it from sibling tools like earthquakes and warnings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like get_weather_warnings. While siblings are different, the description does not provide when/why to choose forecast over warnings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_weather_warnings気象警報・注意報を取得AInspect
指定地域に現在発表されている気象警報・注意報（大雨警報、洪水警報、雷注意報など）を気象庁公式データから取得する。防災判断・イベント開催判断・物流計画などに使う。
| Name | Required | Description | Default |
|---|---|---|---|
| area | Yes | 都道府県・予報区名（例: 東京都 / 大阪府） |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes the tool as retrieving current warnings from JMA data, which is appropriate for a simple read operation. However, it does not disclose data freshness, error handling, or behavior in absence of warnings.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with verb and resource, includes examples and use cases. No redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Completeness is good for a simple tool with one parameter: purpose, data source, and use cases are covered. Return format is not described, but may be inferred; no output schema needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description of the 'area' parameter. The tool description does not add extra parameter details, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves current weather warnings (heavy rain, flood, lightning) from official JMA data for a specified area. It lists example use cases and is distinct from siblings (earthquakes, forecasts).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit use cases (disaster prevention, event planning, logistics) are given. It does not explicitly state when not to use or suggest alternatives, but sibling names imply separate purposes.
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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