This server exposes Portuguese weather, warning, seismic, and UV data from IPMA to MCP-compatible AI clients.
Get daily weather forecasts for Portuguese cities (e.g., Braga, Lisbon) with optional number of days.
Retrieve active meteorological warnings for Portugal.
View recent seismic activity for the continent, Azores, Madeira, or all areas.
List all locations/cities supported by the forecast API.
Access real-time observations from IPMA weather stations.
Obtain UV index forecast data.
Click on "Deploy 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., "@IPMA Weather Server What's the weather forecast for Lisbon this weekend?"
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.
A Model Context Protocol (MCP) server that exposes Portuguese weather, warning and seismic data from the public IPMA API to MCP-compatible AI clients.
Built with Node.js and the official Model Context Protocol SDK.
Live demonstration captured on 2026-09-10. Braga is selected explicitly by its IPMA identifier because Amares is not listed in this endpoint's forecast catalogue. Reproduce the call and view capture details.
The server exposes six MCP tools:
get_weather_forecast - daily weather forecasts for Portuguese locations
get_weather_warnings - current and upcoming meteorological warnings
get_seismic_data - recent seismic activity
get_locations - available forecast locations
get_weather_stations - latest hourly weather station observations
get_uv_forecast - UV index forecasts
All six tools provide validated structured results alongside readable text. Forecast locations can be selected by name or stable IPMA identifier. An optional authenticated Streamable HTTP transport supports explicitly provisioned remote clients.
Data is retrieved directly from the public API provided by the Instituto Português do Mar e da Atmosfera (IPMA).
Related MCP server: IPMA Weather MCP Server
Node.js
JavaScript
Model Context Protocol
@modelcontextprotocol/sdk
node-fetch
Zod contracts and JavaScript checked with TypeScript
IPMA Open Data API
MCP Client
│
│ Model Context Protocol
▼
IPMA MCP Server
│
│ HTTPS
▼
IPMA Open Data APIThe default entry point uses standard input/output. A separate HTTP entry point shares the same MCP handlers, domain contracts, formatting and IPMA client. Only reference catalogues are cached; live measurements are fetched on demand. See the architecture and design decisions.
Node.js 22 or newer (Node.js 24 LTS recommended; .nvmrc and Docker use 24)
npm
An MCP-compatible client
Clone the repository:
git clone https://github.com/brandao-20/ipma-mcp-server.git
cd ipma-mcp-serverInstall the dependencies:
npm ciStart the MCP server:
npm startThe process waits for MCP messages on standard input; it does not start an HTTP server or a command prompt. MCP clients should launch node directly as shown below, keeping standard output reserved for protocol messages. No environment variables or API keys are required.
For Docker, build with docker build -t ipma-mcp-server . and run with docker run --rm -i ipma-mcp-server. Keep standard input open and do not allocate a TTY.
For the optional HTTP transport, follow the authenticated HTTP setup and deployment guide. It requires client credentials and defaults to loopback. A small SDK client demonstrates consuming structured forecast data.
For clients that support local MCP servers, configure the server to run the project's entry point.
Example:
{
"mcpServers": {
"ipma": {
"command": "node",
"args": ["/absolute/path/to/ipma-mcp-server/src/index.js"]
}
}
}Replace the path with the absolute location of the cloned repository.
get_weather_forecastReturns a daily weather forecast for a Portuguese location.
Parameters:
city string required unless locationId is provided
locationId integer required unless city is provided
days integer optional, 1–5 (default: 5)Provide exactly one of city or locationId. Names ignore case, accents and surrounding whitespace. Exact names take precedence; partial names must resolve to a single location. Ambiguous names return supported candidates instead of choosing one silently. The result identifies the selected location and returns only the days available from IPMA. Data contracts and compatibility details.
Example request:
What is the weather forecast for Braga for the next three days?get_weather_warningsReturns current and upcoming yellow, orange and red meteorological warnings for Portugal. Green entries and expired warnings are excluded. Each warning includes its status and UTC validity interval.
Example request:
Are there any active weather warnings in Portugal?get_seismic_dataReturns up to ten recent seismic observations, sorted newest first, from IPMA's feeds covering the past 30 days.
Supported areas:
continent
azores
madeira
allThe optional area defaults to all, which combines the Azores feed (3.json) and the mainland/Madeira feed (7.json), removing duplicate events. Both continent and madeira select the same combined mainland/Madeira feed: IPMA does not provide separate feeds for them. Results state this coverage explicitly. If either request fails, all returns an error rather than an incomplete national result.
Example request:
Show me recent seismic activity in the Azores.get_locationsLists supported forecast locations with stable identifiers, district IDs and coordinates. Optional query and districtId parameters filter the catalogue.
get_weather_stationsReturns up to 15 stations with available measurements at the latest hourly timestamp. Missing measurements (including IPMA's -99 sentinel) are omitted. The response states how many stations are shown.
get_uv_forecastReturns UV index forecasts for up to three dates and ten locations per date, with risk categories and peak UV time intervals in UTC. The response states the display limits; missing UV values are identified as unavailable.
This project uses the public open-data services provided by IPMA — Instituto Português do Mar e da Atmosfera.
API:
https://api.ipma.pt/open-data/No API key is required for the IPMA endpoints used by this project.
Endpoint formats and coverage are documented in the IPMA API reference. Response labels are in English; place names and warning text supplied by IPMA retain their original language.
npm run check
npm test
npm auditTests use Node.js's built-in runner, synthetic IPMA fixtures, local HTTP servers and the official MCP client. They cover contracts, location resolution, data selection, cache expiry, retries, cancellation, HTTP authentication/session isolation and stdio lifecycle without contacting IPMA. npm run check verifies types and syntax without emitting compiled files. npm audit requires registry access.
The CI workflow runs installation, type/syntax checks and tests on Node.js 22 and 24 for pushes and pull requests. The Node.js 24 leg also validates Compose configuration, builds the image and checks MCP over container stdio without network access. The workflow can be started manually; it does not deploy the application. Live IPMA requests are excluded to keep CI independent of upstream availability.
Run npm run test:live for stdio, or npm run test:live:http for a temporary authenticated loopback HTTP server. Both check all six tools, each seismic area and forecasts selected by name and identifier against the live API. They require internet access and may fail when IPMA is unavailable.
To validate Docker locally, run docker build -t ipma-mcp-server:validation . followed by npm run test:container.
Live data depends on IPMA availability and publication times. Only three reference catalogues are cached for up to one hour; there is no stale-data fallback or cache of forecasts, warnings, seismic events or observations.
Each tool call has a 10-second overall deadline and each upstream response a 5 MiB limit. Transient failures may receive one retry within that deadline. Tool failures return isError: true and a structured error; invalid arguments use MCP protocol errors.
Forecast, seismic, station and UV results have the limits described above. Timestamps are displayed in UTC.
smithery.yaml is retained as legacy installation metadata. The supported setup is the local stdio configuration above; no current Smithery listing or hosted service is claimed.
HTTP uses provisioned bearer credentials and in-memory sessions; it does not implement OAuth discovery, persistent sessions or multiple application replicas. Public use requires a configured HTTPS proxy and host.
The architecture document records the implemented evolution, its trade-offs and validation boundaries.
ipma-mcp-server/
├── .github/workflows/ci.yml
├── docs/
│ ├── images/mcp-inspector-braga.png
│ ├── architecture-roadmap.md
│ ├── data-contracts.md
│ ├── demo.md
│ ├── http.md
│ └── inspector.json
├── src/
│ ├── index.js
│ ├── http.js
│ ├── mcp/
│ ├── domain/
│ ├── ipma/
│ └── http/
├── test/
│ ├── server.test.js
│ ├── contracts.test.js
│ ├── resilience.test.js
│ └── http.test.js
├── scripts/
│ ├── smoke.mjs
│ └── container-smoke.mjs
├── examples/http-client.mjs
├── deploy/Caddyfile
├── compose.http.yaml
├── tsconfig.json
├── Dockerfile
├── LICENSE
├── package.json
├── package-lock.json
└── README.mdThis project is licensed under the MIT License.
Listar todas as cidades/locais disponíveis para previsão
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 states it lists locations but doesn't describe traits like whether it's read-only, if it requires authentication, rate limits, or what the output format might be (e.g., list of strings, JSON objects). This is a significant gap for a tool with zero 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence in Portuguese that directly states the tool's function without any fluff. It's front-loaded and appropriately sized, making it easy to understand at a glance.
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 (0 parameters, no annotations, no output schema), the description is minimal but incomplete. It doesn't address behavioral aspects like output format or usage context, which are important even for simple tools. With no annotations or output schema, the description should do more to compensate.
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 0 parameters, and schema description coverage is 100%, so there's no need for parameter details in the description. The baseline for 0 parameters is 4, as the description appropriately doesn't waste space on non-existent parameters, and the schema fully covers the input structure.
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's purpose: 'Listar todas as cidades/locais disponíveis para previsão' (List all cities/locations available for forecasting). It specifies the verb 'listar' and the resource 'cidades/locais', making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_weather_stations', which might also list locations, so it doesn't reach the highest score.
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 sibling tools like 'get_weather_stations' or explain if this is for general location listing versus station-specific data. There's no context on prerequisites or exclusions, leaving usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Obter dados sísmicos recentes
| Name | Required | Description | Default |
|---|---|---|---|
| area | No | Área: 'continent', 'azores', 'madeira', ou 'all' | all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool retrieves data ('obter') which implies a read operation, but doesn't specify whether this requires authentication, has rate limits, returns real-time vs historical data, or what format/volume the data comes in. The 'recentes' qualifier suggests temporal recency but doesn't define the timeframe. For a data retrieval tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
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 - a single three-word phrase in Portuguese. While appropriately brief, it could benefit from slightly more context about what 'recent' means or what format the data returns. However, it's front-loaded with the essential action and resource, with no wasted words or redundant information.
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 moderate complexity (data retrieval with geographic filtering), no annotations, and no output schema, the description is minimally adequate. It identifies the resource and action but leaves unanswered questions about data format, recency definition, authentication needs, and error conditions. The schema covers the single parameter well, but overall completeness is limited for a tool that presumably returns structured seismic data.
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 100% description coverage with a well-documented 'area' parameter including enum-like options and default value. The description doesn't add any parameter information beyond what's in the schema, but with only one parameter and complete schema documentation, this is acceptable. The baseline for 100% coverage with 1 parameter is appropriately high since the schema does all the work.
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 'Obter dados sísmicos recentes' (Get recent seismic data) states a clear verb ('obter' - get) and resource ('dados sísmicos' - seismic data) with a temporal qualifier ('recentes' - recent). However, it doesn't distinguish this tool from potential siblings - while no direct seismic siblings exist, the agent might wonder about differences from geological or environmental data tools. The purpose is understandable but lacks differentiation context.
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. There's no mention of what makes this seismic data tool appropriate compared to weather-related sibling tools (get_weather_forecast, get_weather_warnings) or location tools (get_locations). No prerequisites, constraints, or comparative context is provided to help the agent choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Obter previsão do índice UV
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool gets a UV forecast but doesn't describe what that entails—e.g., whether it returns current or future data, if it requires location input (despite having 0 parameters), potential rate limits, or authentication needs. This leaves significant gaps in understanding how the tool behaves beyond its basic purpose.
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 ('Obter previsão do índice UV') that directly states the tool's purpose without any waste. It's front-loaded and appropriately sized for a simple tool, making it easy to parse quickly. Every word earns its place by conveying 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?
Given the lack of annotations and output schema, the description is incomplete for a tool that might have behavioral complexities. It doesn't explain what the UV forecast includes (e.g., values, times, locations) or how it differs from sibling tools like 'get_weather_forecast'. For a tool with potential contextual nuances among weather-related siblings, more detail is needed to ensure the agent can use it correctly.
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 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to compensate for any parameter gaps, and it appropriately doesn't mention parameters. A baseline of 4 is applied since no parameter information is required, and the description doesn't add unnecessary details.
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 'Obter previsão do índice UV' clearly states the action (obter/get) and resource (previsão do índice UV/UV index forecast), which is adequate. However, it doesn't differentiate from sibling tools like 'get_weather_forecast' or 'get_weather_warnings', leaving ambiguity about scope boundaries. The purpose is understandable but lacks specificity about what distinguishes this UV forecast from other weather-related tools.
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. With siblings like 'get_weather_forecast' and 'get_weather_warnings', there's no indication of whether this tool is for a specific location, time frame, or detail level. It lacks explicit when/when-not instructions or named alternatives, leaving usage context entirely implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Obter previsão meteorológica para uma cidade específica em Portugal
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | Nome da cidade (ex: Lisboa, Porto, Coimbra, Faro, etc.) | |
| days | No | Número de dias de previsão (máximo 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool gets forecasts for cities in Portugal, but doesn't describe what the forecast includes (e.g., temperature, precipitation), how it's formatted, whether it's real-time or cached, rate limits, error handling, or data sources. For a tool with no annotation coverage, this leaves significant gaps in understanding its 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?
The description is a single, efficient sentence in Portuguese that directly states the tool's purpose. It's appropriately sized and front-loaded with the core functionality, with no redundant or verbose language. Every word earns its place by specifying the action, resource, and geographic scope.
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 complexity (2 parameters, no output schema, no annotations), the description is incomplete. It lacks details on what the forecast returns (e.g., data structure, units), behavioral traits like rate limits or errors, and usage context relative to siblings. Without an output schema, the description should ideally hint at return values, but it doesn't, leaving the agent with insufficient information for effective use.
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 description adds no parameter semantics beyond what the input schema provides. The schema has 100% description coverage, with clear documentation for 'city' (city name with examples) and 'days' (number of forecast days, maximum 10, default 5). Since schema coverage is high (>80%), the baseline score is 3, as the description doesn't compensate with additional context like valid city formats or day-range implications.
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's purpose: 'Obter previsão meteorológica para uma cidade específica em Portugal' (Get weather forecast for a specific city in Portugal). It specifies the verb ('obter' - get) and resource ('previsão meteorológica' - weather forecast), and distinguishes from siblings by focusing on city-specific forecasts rather than locations, seismic data, UV forecasts, stations, or warnings. However, it doesn't explicitly differentiate from all siblings (e.g., 'get_weather_warnings' might also be city-specific).
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 when to prefer this over 'get_weather_warnings' for alerts, 'get_uv_forecast' for UV data, or 'get_weather_stations' for station-specific data. There's no context about prerequisites, limitations, or typical use cases beyond the basic purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Obter dados de observação das estações meteorológicas
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 states the tool retrieves observation data, implying a read-only operation, but doesn't clarify aspects like data freshness, rate limits, authentication needs, or what happens if no data is available. This leaves significant gaps in understanding the tool's 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?
The description is a single, efficient sentence in Portuguese that directly states the tool's purpose without unnecessary words. It's appropriately sized for a tool with no parameters, though it could be slightly more informative without losing conciseness.
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 (0 parameters, no output schema, no annotations), the description is minimal. It lacks details on what 'dados de observação' includes (e.g., temperature, humidity), how data is returned, or any behavioral traits, making it incomplete for effective agent use despite the low complexity.
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 input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description doesn't add parameter details, but this is acceptable as there are no parameters to document. A baseline of 4 is appropriate since the schema fully covers the absence of parameters.
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 'Obter dados de observação das estações meteorológicas' (Get observation data from weather stations) states a clear verb ('Obter') and resource ('estações meteorológicas'), but it's vague about what specific data is retrieved and doesn't distinguish from siblings like 'get_weather_forecast' or 'get_weather_warnings'. It provides a basic purpose but lacks specificity.
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 alternatives such as 'get_weather_forecast' for forecasts or 'get_weather_warnings' for alerts. The description implies usage for observational data but doesn't specify contexts, exclusions, or prerequisites, leaving the agent to infer based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Obter avisos meteorológicos ativos em Portugal
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but provides minimal behavioral information. It doesn't disclose whether this is a read-only operation (implied by 'get'), what data format is returned, whether there are rate limits, authentication requirements, or how current the warning data is. The description states the basic purpose but lacks operational context needed for effective tool use.
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?
Perfectly concise single sentence in Portuguese that communicates the essential purpose: get active weather warnings in Portugal. No wasted words, no redundant information, and front-loaded with the core action. The structure is optimal for a simple tool with no parameters.
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 annotations, no output schema, and a simple zero-parameter design, the description is insufficiently complete. It doesn't explain what format the warnings come in (text, codes, severity levels), whether it returns all warnings or needs filtering, temporal aspects (how 'active' is defined), or error conditions. For a weather warning tool where users need to understand the nature and format of warnings, this leaves significant gaps.
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 with 100% schema description coverage, so the schema fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist. It earns a 4 rather than 5 because while it correctly avoids parameter discussion, it doesn't explicitly state 'no parameters required' which could help clarify the zero-parameter design.
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 ('Obter' meaning 'Get') and resource ('avisos meteorológicos ativos' meaning 'active weather warnings') with geographic scope ('em Portugal' meaning 'in Portugal'). It distinguishes from siblings like get_weather_forecast (forecasts vs warnings) and get_seismic_data (different hazard type). However, it doesn't specify whether it returns all warnings or filtered subsets, which prevents a perfect score.
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. The description implies it's for active warnings in Portugal, but doesn't clarify when to choose this over get_weather_forecast (which might include warning information) or get_seismic_data (for different hazard types). No mention of prerequisites, frequency of updates, or limitations.
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.
v1.0.0get_locationsget_seismic_dataget_uv_forecastget_weather_forecastget_weather_stationsget_weather_warningsScored across 6 tools
Each tool has a clearly distinct purpose with no overlap: listing locations, seismic data, UV forecast, weather forecast, station observations, and weather warnings. The descriptions specify different data types and targets, making misselection unlikely.
All tools follow a consistent 'get_<resource>' naming pattern (e.g., get_locations, get_weather_forecast). This uniformity makes the tool set predictable and easy to understand for an agent.
With 6 tools, the server is well-scoped for weather and seismic data in Portugal. Each tool serves a specific function without redundancy, fitting the domain appropriately and avoiding bloat.
The tools cover key aspects like forecasts, warnings, stations, and seismic data, but there are minor gaps such as no historical weather data or detailed location-specific queries beyond forecasts. However, core workflows are well-supported.
IPMA Portugal MCP — weather, UV, sea state, and earthquakes from the
Global weather via Open-Meteo: forecast, historical, marine, air quality, geocoding, elevation.
Autocomplete Portuguese addresses and look up postal codes (CP7). Free remote MCP, no API key.
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