get_weather_observations
Get live MeteoLux weather observations at Luxembourg-Airport: temperature, wind, pressure, humidity, visibility.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Luxembourg MCP
TDQS
A4.1/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Mentions 'live' observations but does not disclose update frequency, caching, or that it is a read-only operation. With no annotations, description could provide more behavioral context.
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?
Single sentence, front-loaded with key information, no superfluous text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and simple output, the description sufficiently covers location, data types, and live nature. No output schema required.
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?
No parameters; schema coverage is 100%. Description adds no param info but not needed. Baseline 4 for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states verb ('Get'), specific resource ('live MeteoLux weather observations at Luxembourg-Airport'), and lists included data fields. Distinguishes from sibling tools like get_weather_alerts by focusing on current observations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
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 vs alternatives (e.g., get_weather_alerts). Usage is implied but not contrasted with sibling tools.
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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TDQS
B3.4/5.0
Disambiguation5/5
Each tool targets a distinct domain or data source (e.g., air quality, parking, transit, legislation), with clear differentiation even within similar categories (e.g., get_city_parking vs get_cfl_parking). No two tools overlap in purpose.
Naming Consistency5/5
Tool names follow a consistent verb_noun pattern: get_*, search_*, list_*, geocode_*, reverse_geocode_. All use lowercase with underscores, making them predictable and easy to parse.
Tool Count4/5
27 tools is relatively high but each serves a distinct public data endpoint. The count feels appropriate given the breadth of Luxembourg's official datasets, though it could be streamlined slightly without loss of functionality.
Completeness4/5
Covers a wide range of domains including geography, transport, environment, legislation, elections, and statistics. Minor gaps exist (e.g., public transport schedules beyond stops, cultural events), but the set is comprehensive for a national data API.