The AEMET-MCP server enables access to Spain's meteorological and climate data via the Spanish State Meteorological Agency (AEMET) API for MCP-compatible clients. You can:
Get daily weather forecasts for specific Spanish municipalities
Retrieve specific weather station data by station ID
List all available weather stations
Access historical weather data for stations between specified dates (YYYY-MM-DD)
Obtain monthly climate summaries for specific weather stations by year and month
Resolve beach names and codes from partial inputs
Query beach conditions and UV indices for specific beaches or provinces
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., "@AEMET-MCP what's the weather like in Barcelona today?"
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.
AEMET is the State Meteorological Agency of Spain.
Aemet-mcp allows you to retrieve historical climate data and meteorological information from the AEMET API directly from Claude AI and other MCP compatible clients, using the Model Context Protocol (MCP).
Aemet-mcp is an MCP server that exposes tools enabling LLMs to query data from weather stations across Spain.
It includes secure handling of API keys and resource JSON files for using supporting data.
Related MCP server: Weather MCP
Query for historical daily values (temperature, wind, precipitation, etc.)
Access to monthly climate summaries by station.
Filtering by year, month, and AEMET station code.
Query beach conditions, including ultraviolet radiation indices.
Rainfall data analysis prompt for Spanish municipalities with historical precipitation data
Responses ready for use in JSON format.
To install AEMET Weather Data Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @AnCode666/aemet-mcp --client claudePython 3.10 or higher.
uv package manager.
The first step is to install uv, a package manager for Python.
It can be installed from the command line.
On macOS and Linux:
curl -LsSf https://astral.sh/uv/install.sh | shOn Windows:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"You can also install it with pip:
pip install uvFor more information about installing uv, visit the uv documentation.
You can also run AEMET-MCP using Docker:
Docker installed on your system
Build the Docker image:
docker build -t aemet-mcp .Run the container:
docker run -e AEMET_API_KEY=YOUR_AEMET_API_KEY aemet-mcpReplace YOUR_AEMET_API_KEY with your actual API key from AEMET.
To use the Docker version with Claude Desktop, add this configuration to your claude_desktop_config.json:
"aemet_mcp_docker": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"-e", "AEMET_API_KEY=YOUR_AEMET_API_KEY",
"aemet-mcp"
]
}Once uv is installed, you can use the MCP server from any compatible client such as Claude for Desktop, in which case the steps to follow are:
Go to Claude > Settings > Developer > Edit Config > claude_desktop_config.json
Add the following block inside "mcpServers":
"aemet_mcp_": {
"command": "uvx",
"args": [
"aemet_mcp"
],
"env": {
"AEMET_API_KEY": "YOUR_AEMET_API_KEY"
}
}Get a free API key from AEMET at: https://opendata.aemet.es/centrodedescargas/altaUsuario
Replace YOUR_AEMET_API_KEY with your actual API key (leave the quotes).
If you already have another MCP server configured, separate each with a comma ,.
In general, to integrate it into any other MCP-compatible client such as Cursor, CODEGPT, or Roo Code, simply go to the MCP server configuration of your client and add the same code block.
Once properly configured, you can ask things like:
"What's the weather like in Seville?"
"Give me a list of the beaches in the province of Málaga"
"Tell me the radiation levels at Maspalomas beach for tomorrow"
"Give me the historical rainfall data for Albacete between January 1st, 2020 and February 1st, 2020"
"Give me a list of the weather stations within a 50 km radius from the coordinates lat:40.4165, lon:-3.70256"
The server includes a specialized prompt for analyzing historical precipitation data for Spanish municipalities. Use the obtener_datos_lluvia_municipio prompt with:
obtener_datos_lluvia_municipio("Madrid", "2023-01-01", "2023-12-31")This prompt provides structured guidance for meteorological analysis, including:
Municipality code search and validation
Nearest weather station identification
Historical precipitation data retrieval
Statistical analysis and trend identification
Climate pattern analysis with seasonal variations
Data visualization recommendations
Find weather stations within a given radius (in km) from a given geographic coordinate.
Args: lat: Latitude in decimal degrees (e.g., 43.36) lon: Longitude in decimal degrees (e.g., -8.41) radio_km: Search radius in kilometers
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | ||
| lon | Yes | ||
| radio_km | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose behavioral traits such as whether results are sorted, any limits, how invalid coordinates are handled, or what the output looks like. The description is minimal.
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?
Description is very concise: one sentence plus a brief Args list. No extraneous information. Every line serves a purpose.
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 lack of output schema and annotations, the description should provide more context. It doesn't describe what the tool returns (station IDs, names, coordinates?) or potential errors. Incomplete for an agent to fully understand usage.
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?
Input schema properties have no descriptions (0% coverage). The description adds units (decimal degrees for lat/lon, km for radius) and examples for lat/lon, which is helpful. However, it does not specify valid ranges or constraints, so it is only partially compensatory.
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 explicitly states the tool finds weather stations within a radius from a coordinate. It uses a specific verb ('find') and resource ('weather stations'), and the radius and coordinate constraints are clear. This distinguishes it from sibling tools like get_station_list or get_station_data.
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 vs alternatives. The agent must infer from tool names. No mention of when-not-to-use or prerequisites like requiring valid coordinates.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Query information on beaches or UV index from AEMET.
Args: name_or_code: Partial or full name of the beach, or its BEACH_ID. Also accepts 'list' or 'list:'. dias_frc: Number of forecast days, starting form 0, which means 0 days from today, to 4, which means 4 days from today. query_type: 'beach' for forecast, 'UV_index' for UV index, must be in english.
Returns: Requested information or list of matches.
| Name | Required | Description | Default |
|---|---|---|---|
| nombre_o_codigo | Yes | ||
| dias_frc | Yes | ||
| tipo_consulta | No | playa |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden. It mentions query_type options and return type ('Requested information or list of matches'), but does not disclose side effects, authentication needs, rate limits, or error conditions. It is moderately transparent but incomplete.
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 concise—three short sentences for purpose and parameters, plus a returns line. It is front-loaded and free of extraneous text, earning its space.
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?
The tool has 3 params and no output schema. Description explains all params and return type generically, but does not specify output format or handling of missing data. Additionally, parameter names in the schema are in Spanish while description uses English, potentially causing confusion.
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 0%, so description must compensate. It explains each parameter: name_or_code can be partial name, BEACH_ID, or 'list:<province>'; dias_frc range 0-4; query_type values 'beach' or 'UV_index' with English requirement. This adds significant meaning beyond the schema's minimal field names.
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 queries information on beaches or UV index from AEMET, with specific verb and resource. It distinguishes from siblings like get_station_data and get_daily_forecast which cover different weather data.
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 explains parameters and their use (e.g., 'list' for listing, query_type for beach vs UV), but does not explicitly state when to avoid this tool or point to alternatives among siblings. No exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the daily weather forecast for a Spanish municipality.
Args: municipality_code: AEMET municipality code (e.g., "28079" for Madrid)
| Name | Required | Description | Default |
|---|---|---|---|
| municipality_code | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states it returns a 'daily weather forecast.' It omits critical behavioral details such as units, time range, data source, safety (read-only), and potential limitations (e.g., only for today or extended forecast).
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 extremely concise (two sentences) with no wasted words. It front-loads the purpose and includes parameter explanation. Minor improvement would be to mention the output format briefly.
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 one-parameter tool, the description is incomplete. It does not specify output format, units, or whether multiple days are returned. Since there is no output schema, more content is needed to ensure proper tool usage.
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 0%, so the description must compensate. It explains 'municipality_code' as an AEMET code with an example ('28079' for Madrid). This adds value beyond the schema's type/title, but lacks guidance on how to obtain the code (e.g., via search_municipality_code).
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 'Get the daily weather forecast for a Spanish municipality,' specifying the verb (get), resource (daily weather forecast), and scope (Spanish municipality). This purpose is distinct from sibling tools like get_station_data or get_beach_data_uv.
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 guidance on when to use this tool versus others (e.g., search_municipality_code or get_historical_data). The description does not mention prerequisites or alternative approaches, leaving the agent with no decision context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Obtain historical meteorological data for a specific station.
Args: station_id: Identifier of the station (e.g. "3195" for Madrid Retiro) start_date: Start date in format YYYYY-MM-DD end_date: End date in format YYYYY-MM-DD
| Name | Required | Description | Default |
|---|---|---|---|
| station_id | Yes | ||
| start_date | Yes | ||
| end_date | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only states it obtains data but does not disclose any behavioral traits such as read-only nature, data availability limitations, rate limits, or error handling. Misses opportunity to inform agent about API constraints.
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?
Relatively concise docstring with purpose and parameter list. Every sentence adds value, but the format could be more streamlined without the 'Args' boilerplate.
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?
No output schema; description does not explain what the returned data looks like (e.g., fields, structure). Lacks examples or details on station availability, making it incomplete for an agent to fully understand usage.
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 has 0% description coverage; description compensates by explaining station_id (with example), start_date and end_date formats. Adds significant meaning beyond schema titles, though date format has a typo (5 Y's).
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?
Clearly states 'Obtain historical meteorological data for a specific station', specifying verb (obtain), resource (historical meteorological data), and scope (specific station). Distinguishes from siblings like get_daily_forecast (forecast) and get_station_data (likely current/summary).
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?
Implies usage for historical data ranges via name and description, but no explicit guidance on when to use this vs siblings (e.g., get_station_data, monthly_climate_data). No exclusion criteria or alternatives mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Obtain specific weather data for a weather station.
Args: station_id: Station identifier (e.g., "8416Y" for Valencia))
| Name | Required | Description | Default |
|---|---|---|---|
| station_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only says 'obtain data.' It omits important behavioral traits like whether it's read-only, authentication needs, or rate limits. The return format or data completeness is not disclosed.
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 brief and front-loaded with the purpose. The Args section is clear, but the example formatting is slightly messy (e.g., extra closing parenthesis). Overall concise without wasted 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?
No output schema is provided, and the description only vaguely mentions 'specific weather data' without defining what is returned. For a data retrieval tool, this is incomplete and leaves the agent uncertain about the result.
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 sole parameter station_id is explained with an example ('8416Y' for Valencia), adding significant meaning beyond the schema. Despite 0% schema coverage, the description compensates by showing the expected format.
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 obtains specific weather data for a given station, which distinguishes it from listing stations or other data types. However, it does not explicitly differentiate from forecast or historical data tools, leaving some ambiguity.
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 guidance on when to use this tool versus siblings like get_daily_forecast or get_historical_data. The description lacks context for appropriate usage scenarios or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a list of all available weather stations or filter by one or more search terms, including approximate matches.
Args: search_terms: Optional terms (space or comma separated) to filter stations by name or province.
| Name | Required | Description | Default |
|---|---|---|---|
| search_terms | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It mentions approximate matching but lacks details on pagination, sorting, or read-only nature. Minimal disclosure beyond basic behavior.
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?
Description is short and direct, but includes a redundant 'Args' block that mirrors schema. Could be slightly more concise without losing clarity.
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 tool simplicity (1 optional param, no output schema), description covers purpose and parameter usage fully. No critical gaps for selecting or invoking the tool.
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?
Parameter 'search_terms' is well-described: format (space/comma separated) and filtering scope (name or province). Adds significant value beyond schema (0% coverage).
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 it retrieves a list of weather stations, optionally filtered by search terms with approximate matching. It distinguishes from siblings like find_nearby_stations which is location-based.
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?
Implies use for listing stations vs. other tools like find_nearby_stations or get_station_data, but does not explicitly state when not to use or provide alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve monthly climatological data for a specific weather station.
Args: station_id: Weather station identifier (e.g., "3195" for Madrid Retiro). year: Year (YYYY). month: Month (1-12).
Returns: A JSON with the monthly climate summary.
| Name | Required | Description | Default |
|---|---|---|---|
| station_id | Yes | ||
| year | Yes | ||
| month | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description indicates a read operation ('retrieve') but doesn't explicitly state it's read-only or disclose any side effects. For a read tool without annotations, the description should explicitly mention it is non-destructive.
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?
Concise docstring format with Args and Returns. No unnecessary words. Efficient.
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?
Tool is simple with 3 parameters and no output schema. Description covers input parameters and return type (JSON summary), but lacks usage context and output schema details. Adequate but could be improved.
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 0%, description adds meaning by providing an example station ID ('3195' for Madrid Retiro) and specifying year format (YYYY) and month range (1-12). However, it doesn't clarify valid ranges or other constraints.
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?
Clear verb 'Retrieve' and specific resource 'monthly climatological data' for a weather station. Distinguishes from siblings like get_daily_forecast or get_historical_data by specifying monthly granularity, though no explicit differentiation.
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 guidance on when to use this tool vs alternatives like get_historical_data. The description does not mention any context or constraints.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Spanish municipalities by name or province (accent-insensitive, typo-tolerant).
Args: nombre: Partial or approximate name of a municipality or province.
Returns: A list of matching municipalities with their codes and provinces.
| Name | Required | Description | Default |
|---|---|---|---|
| nombre | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses accent-insensitivity and typo-tolerance, but does not mention pagination, error behavior, or limitations. It is adequate but not thorough.
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 extremely concise, front-loading the purpose and using a clear Args/Returns structure. Every sentence adds value with no wasted 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 explains the return value as a 'list of matching municipalities with their codes and provinces,' which is sufficient for an agent to understand the tool's output. It could mention default behavior for unmatched queries but is otherwise 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?
The schema has 0% description coverage, and the description provides meaningful semantics for the only parameter 'nombre': 'Partial or approximate name of a municipality or province.' This compensates well for the lack of schema description.
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 searches Spanish municipalities by name or province, and highlights accent-insensitive and typo-tolerant search. It is distinct from siblings which are weather/station/beach related.
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 does not provide any guidance on when to use this tool vs alternatives, nor does it mention prerequisites or postconditions. While the context of siblings makes usage somewhat clear, explicit guidance is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search beaches by name, province, or municipality.
Args: nombre: Search string (accent-insensitive) tipo: One of 'playa', 'provincia', or 'municipio'
| Name | Required | Description | Default |
|---|---|---|---|
| nombre | Yes | ||
| tipo | No | playa |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden for behavioral transparency. It only mentions accent-insensitive search for 'nombre' but lacks details on rate limits, required permissions, or return format. Behavioral traits like output structure or error handling are omitted.
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 very concise (3 short lines) and front-loaded with the main action. However, it is so brief that it sacrifices completeness. It earns a 4 for efficient use of words but not a 5 due to missing context.
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 absence of an output schema and low schema coverage, the description is insufficient. It does not explain the return structure, pagination, or how to interpret results. A tool with 2 parameters and no output schema needs more descriptive context.
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 0%, so the description must compensate. It lists parameter names and types, and the 'tipo' enum is implicit but not explicitly explained (e.g., 'playa' maps to beach name). The description adds minimal semantics beyond the schema's enum values.
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 'Search beaches by name, province, or municipality,' which specifies the verb and resource. However, it does not differentiate from sibling tools like 'search_municipality_code', leaving ambiguity about when each is appropriate.
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 guidance is provided on when to use this tool versus sibling tools. There is no mention of prerequisites, limitations, or alternative tools, which is a significant gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0find_nearby_stationsget_beach_data_uvget_daily_forecastget_historical_dataget_station_dataget_station_listmonthly_climate_datasearch_municipality_codesolve_beach_codeEvery tool targets a distinct resource or operation: stations (find, list, get data, historical, monthly), beaches (search, get data), daily forecast, and municipality search. No overlapping purposes.
Naming conventions are mixed: some use 'get_', others 'find_', 'search_', 'solve_', and one is adjective_noun ('monthly_climate_data'). While all are descriptive, the pattern is inconsistent.
Nine tools is well-scoped for a weather data API covering stations, beaches, forecasts, historical data, and municipality lookup. Each tool serves a clear purpose without unnecessary duplication.
The tool set covers core weather queries: station listings, current data, daily forecasts, historical and monthly climate, beach info, and UV index. Missing are alerts or radar, but the surface is largely complete for common use cases.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
An MCP server for weather information by @kulybaba
An MCP server for weather information by @kulybaba
Hosted MCP server connecting claude.ai, ChatGPT and other AI apps to your own computer
Hosted MCP server for Xweather weather data: conditions, forecasts, alerts, and more.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/AnCode666/aemet-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
