Japanese Weather MCP Server
The Japanese Weather MCP Server provides access to detailed weather forecasts for Japanese cities using Japan Meteorological Agency data via the weather.tsukumijima.net API.
π€οΈ Get weather forecasts by city ID - Use
get_weather_forecasttool for any Japanese city with its specific IDπ Find available cities - Use
get_available_city_idstool to retrieve all city IDs and namesπ Get weather by city name - Use
get_weather_by_city_nametool for major cities like Tokyo, Osaka, Kyotoπ’ Access detailed weather data - Temperature (max/min in Celsius and Fahrenheit), precipitation chance, wind conditions, and wave height
π§ Reliable integration - Comprehensive error handling and rate limiting for stable API usage
Allows manual installation of the MCP server by cloning the repository from GitHub, with instructions for building and running the server.
Supports installation and management of the MCP server dependencies through npm commands for building, testing, and running the server.
Provides weather icons in SVG format as part of the weather forecast data structure.
References the primary_area.xml resource from weather.tsukumijima.net for additional city IDs beyond those listed in the common city table.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Japanese Weather MCP Serverwhat's the weather forecast for Tokyo tomorrow?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Japanese Weather MCP Server
A Model Context Protocol (MCP) server that provides access to Japanese weather forecasts using the weather.tsukumijima.net API.
Features
π€οΈ Get weather forecasts for Japanese cities
π Support for major Japanese cities (Tokyo, Osaka, Kyoto, etc.)
π Detailed weather information including temperature, precipitation chance, and wind conditions
π’ Data sourced from Japan Meteorological Agency
π§ Easy-to-use MCP tools for weather queries
Related MCP server: MCP Weather
Installation
Installing via Smithery
To install japanese-weather-mcp for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @kongyo2/japanese-weather-mcp --client claudeManual Installation
Clone this repository:
git clone https://github.com/kongyo2/Japanese-Weather-MCP
cd weather-mcp-serverInstall dependencies:
npm installBuild the project:
npm run buildUsage
Development Mode
Run the server in development mode:
npm run devProduction Mode
Build and run the server:
npm run build
npm startAvailable Tools
1. get_weather_forecast
Get weather forecast for any Japanese city using its city ID.
Parameters:
cityId(string): City ID for the Japanese city (e.g., '130010' for Tokyo)
Example:
{
"cityId": "130010"
}2. get_available_city_ids
Get a list of available city IDs for common Japanese cities.
Parameters: None
Returns: List of available city IDs with their corresponding names.
3. get_weather_by_city_name
Get weather forecast for common Japanese cities by name.
Parameters:
cityName(enum): Name of the Japanese cityOptions: TOKYO, OSAKA, KYOTO, FUKUOKA, SAPPORO, NAGOYA, YOKOHAMA, KOBE, KAWASAKI, HIROSHIMA
Example:
{
"cityName": "TOKYO"
}Common City IDs
City | ID | Prefecture |
Tokyo | 130010 | Tokyo |
Osaka | 270000 | Osaka |
Kyoto | 260010 | Kyoto |
Fukuoka | 400010 | Fukuoka |
Sapporo | 016010 | Hokkaido |
Nagoya | 230010 | Aichi |
Yokohama | 140010 | Kanagawa |
Kobe | 280010 | Hyogo |
Kawasaki | 140020 | Kanagawa |
Hiroshima | 340010 | Hiroshima |
For more city IDs, visit: https://weather.tsukumijima.net/primary_area.xml
Weather Data Structure
The weather forecast includes:
Basic Information:
Publication time and office
Location details (prefecture, city, district)
Forecast Data (3 days):
Weather condition (telop)
Temperature (max/min in Celsius and Fahrenheit)
Precipitation chance (6-hour intervals)
Detailed weather information
Wind conditions
Wave height (for coastal areas)
Additional Information:
Weather icons (SVG format)
Links to official JMA weather pages
API Source
This MCP server uses the weather.tsukumijima.net API, which provides:
Livedoor Weather API compatible format
Data from Japan Meteorological Agency
No API key required
Free to use
Rate Limiting
Please be respectful of the API:
Use a custom User-Agent header
Avoid rapid successive requests
Maintain at least 0.5-second intervals between requests
Error Handling
The server includes comprehensive error handling:
Network connectivity issues
Invalid city IDs
API service unavailability
Data parsing errors
Development
Project Structure
weather-mcp-server/
βββ src/
β βββ server.ts # Main MCP server implementation
β βββ weather-api.ts # Weather API client
βββ package.json
βββ tsconfig.json
βββ README.mdBuilding
npm run buildTesting
npm testLinting
npm run lintFormatting
npm run formatLicense
MIT License - see LICENSE file for details.
Contributing
Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request
Acknowledgments
weather.tsukumijima.net for providing the weather API
Japan Meteorological Agency for the weather data
FastMCP for the MCP framework
Support
If you encounter any issues or have questions, please:
Check the weather.tsukumijima.net documentation
Review the available city IDs
Ensure proper rate limiting
Open an issue in this repository
Available Tools
3 toolsget_available_city_idsBRead-only
Get a list of available city IDs for Japanese cities
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, indicating this is a safe read operation with a closed set of results. The description adds value by specifying the geographic scope ('Japanese cities'), which isn't captured in annotations. However, it doesn't provide additional behavioral context like response format, pagination, or rate limits. No contradiction exists with annotations.
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 description is a single, clear sentence that efficiently communicates the core purpose without any wasted words. It's appropriately sized for a simple tool with no parameters, and the information is front-loaded with the essential action and resource.
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 zero-parameter read-only tool with annotations covering safety and scope, the description provides adequate context about what resource is returned. However, without an output schema, it doesn't specify the format of the returned list (e.g., array of strings/numbers), which could be helpful. The description is complete enough for basic understanding but lacks detail about the output structure.
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?
The tool has zero parameters, and the input schema has 100% description coverage (though empty). The description appropriately doesn't discuss parameters since none exist, which is correct for this case. No additional parameter semantics are needed, so this meets the baseline expectation for zero-parameter tools.
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 verb ('Get') and resource ('list of available city IDs for Japanese cities'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'get_weather_by_city_name' or 'get_weather_forecast', but the resource specificity (city IDs vs weather data) provides implicit distinction. The description avoids tautology by not just restating 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.
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. It doesn't mention that this tool might be used to obtain valid city IDs before calling weather-related sibling tools, nor does it specify any prerequisites or exclusions. The usage context is implied by the resource type but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_weather_by_city_nameBRead-only
Get weather forecast for common Japanese cities by name
| Name | Required | Description | Default |
|---|---|---|---|
| cityName | Yes | Name of the Japanese city |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true (safe read) and openWorldHint=true (supports flexible queries), so the description doesn't need to repeat these. It adds value by specifying 'common Japanese cities', which clarifies scope beyond what annotations provide, but lacks details on rate limits, error handling, or response format.
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 description is a single, efficient sentence that front-loads the core purpose ('Get weather forecast') without unnecessary words. Every part of the sentence contributes to clarifying the tool's function, making it highly concise and well-structured.
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 the tool's simplicity (1 parameter, 100% schema coverage, annotations provided), the description is adequate but has gaps. It lacks output schema details (e.g., forecast format) and doesn't fully address sibling tool differentiation, which could leave the agent uncertain in complex scenarios.
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 description coverage is 100%, with the parameter 'cityName' fully documented in the schema (including enum values and description). The description adds marginal context by emphasizing 'common Japanese cities', but doesn't provide additional syntax or format details beyond what the schema already covers.
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 action ('Get weather forecast') and target ('common Japanese cities by name'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'get_weather_forecast' or 'get_available_city_ids', leaving some ambiguity about when to choose this tool over alternatives.
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?
The description provides minimal guidance, mentioning 'common Japanese cities by name' which implies usage for those cities, but it doesn't specify when to use this tool versus siblings like 'get_weather_forecast' or 'get_available_city_ids'. No explicit alternatives, exclusions, or contextual rules are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_weather_forecastARead-only
Get weather forecast for a Japanese city using city ID
| Name | Required | Description | Default |
|---|---|---|---|
| cityId | Yes | City ID for the Japanese city (e.g., '130010' for Tokyo, '270000' for Osaka) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the agent knows this is a safe read operation with open-world assumptions. The description adds useful context about the geographic scope ('Japanese city') but doesn't provide additional behavioral details like rate limits, error conditions, or response format.
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 description is a single, efficient sentence that communicates the essential information without any wasted words. It's appropriately sized for a simple tool with one parameter and good annotations.
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 operation with good annotations (readOnlyHint, openWorldHint) and 100% schema coverage, the description provides adequate context. However, without an output schema, it doesn't describe what the forecast response contains, which would be helpful for agent planning.
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 description coverage is 100% with the cityId parameter fully documented in the schema. The description adds minimal value beyond what's already in the schema - it mentions 'using city ID' but doesn't provide additional syntax, format, or usage details. Baseline 3 is appropriate when schema does the heavy lifting.
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 specific action ('Get weather forecast'), the resource ('for a Japanese city'), and the method ('using city ID'). It distinguishes from sibling tools by specifying the city ID parameter approach rather than city name or available IDs.
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?
The description provides clear context about when to use this tool ('using city ID'), which implicitly suggests alternatives like the sibling tool 'get_weather_by_city_name'. However, it doesn't explicitly state when NOT to use this tool or provide explicit comparison guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
3 tool updates
v1.0.0- First observed
get_available_city_ids - First observed
get_weather_by_city_name - First observed
get_weather_forecast
TDQS
The tools have some overlap in purpose, as both get_weather_by_city_name and get_weather_forecast retrieve weather forecasts, but they differ in input parameters (city name vs. city ID). The get_available_city_ids tool is distinct, providing city IDs. Descriptions help clarify the differences, but an agent might initially confuse the two weather tools.
All tool names follow a consistent verb_noun pattern with snake_case, starting with 'get_' for retrieval actions. The naming is predictable and readable, with no deviations in style or convention across the set.
With only 3 tools, the server feels thin for a weather domain that typically includes more operations like historical data or alerts. However, it covers basic forecast retrieval and city ID listing, which is borderline but functional for minimal use cases.
The server provides core forecast retrieval and city ID listing, but there are notable gaps such as missing historical weather data, alerts, or multi-day forecast options. It covers basic needs but lacks comprehensive lifecycle coverage for a weather service domain.
Maintenance
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