IPMA Weather Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
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.
Naming Consistency5/5
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.
Tool Count5/5
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.
Completeness4/5
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.
Average 2.9/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It only states what the tool does ('obter dados sísmicos recentes') without revealing any behavioral traits such as data freshness guarantees, rate limits, authentication requirements, error conditions, or response format. For a tool with zero annotation coverage, this represents significant gaps in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single phrase in Portuguese. While appropriately brief, it's arguably too minimal given the lack of supporting context elsewhere. Every word earns its place, but more content might be warranted for completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's apparent simplicity (1 parameter, 100% schema coverage) but complete lack of annotations and output schema, the description is insufficient. It doesn't explain what 'recent' means, what format the seismic data returns, or any behavioral aspects. For a data retrieval tool with no structured output documentation, the description should provide more context about the nature of the returned data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'area' fully documented in the schema (including type, description with allowed values, and default). The description adds no parameter information beyond what the schema already provides, so it meets the baseline of 3 for adequate coverage when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Obter dados sísmicos recentes' clearly states the action ('obter' = get) and resource ('dados sísmicos recentes' = recent seismic data), providing a basic purpose. However, it lacks specificity about what constitutes 'recent' data and doesn't differentiate from sibling tools, which are all weather-related (locations, UV forecast, weather forecast, stations, warnings) - making this tool distinct by domain but not by detailed function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, appropriate contexts, or exclusions. While sibling tools are weather-focused (suggesting this is for seismic events), the description fails to explicitly state this distinction or provide usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal information. It states the tool gets UV forecasts but doesn't describe what data is returned (e.g., numerical values, risk levels, timeframes), whether it requires location input (though parameters are empty), or any limitations like rate limits or data sources. The description is too vague to guide the agent on what to expect from 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence ('Obter previsão do índice UV') that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action. However, it could be more structured by including key details like scope or output, but given its brevity, it efficiently communicates the basic intent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a forecasting tool with no annotations, no output schema, and sibling tools like 'get_weather_forecast', the description is incomplete. It doesn't explain what the UV forecast includes (e.g., values, units, time periods), how it differs from weather forecasts, or what the return format is. For a tool that likely provides detailed data, this leaves significant gaps for the agent to understand its full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The 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 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.
Purpose3/5
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 establishes the basic purpose. However, it doesn't differentiate this tool from its sibling 'get_weather_forecast' or specify what kind of UV forecast it provides (current, daily, hourly, location-specific). The purpose is understandable but lacks specificity about scope and differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_weather_forecast' (which might include UV data) or other weather-related siblings. There's no mention of prerequisites, context for UV forecasting needs, or exclusions. The agent must infer usage from the tool name alone without any explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
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.
Conciseness4/5
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.
Completeness2/5
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.
Parameters4/5
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.
Purpose3/5
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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters4/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters4/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.

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