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AEMET-MCP

AEMET-MCP

by AnCode666
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_station_data

Retrieve current weather data from any AEMET weather station using its unique identifier.

Instructions

Obtain specific weather data for a weather station.

Args: station_id: Station identifier (e.g., "8416Y" for Valencia))

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "station_id": "<station_id>"
    },
    "name": "get_station_data"
  }
}

Input Schema

NameRequiredDescriptionDefault
station_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3/5.0
Behavior2/5

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.

Conciseness4/5

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.

Completeness2/5

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.

Parameters4/5

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.

Purpose4/5

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.

Usage Guidelines2/5

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.

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