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mcp-edf

mcp-edf

by mgcrea

EDF: Weather

edf_get_weather
Read-only

Fetch average outdoor temperature and sky conditions per day or month from a home weather station to compare weather with EDF electricity and gas consumption.

Instructions

The weather at the home's weather station, as Suivi Conso shows it beside the consumption: the average outdoor temperature per day (to 0.1 °C) with the sky (sunny, sunny spells, overcast, light rain…), or per month (whole °C). Use it to read consumption against the weather: heating demand follows the temperature, cooling the heat, and a sunny day lowers heating needs. The sky is one coarse label per day; for sunshine hours or solar radiation use an external weather archive. Same Paris-time days as the consumption tools, so rows line up with edf_get_electricity_consumption and edf_get_gas_consumption.

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": {
      "from": "<from>"
    },
    "name": "edf_get_weather"
  }
}

Input Schema

NameRequiredDescriptionDefault
toNoLast day, INCLUSIVE, as YYYY-MM-DD, e.g. 2026-10-31. Defaults to yesterday: the meters report once a day, and a day's figures usually land the next morning around 08:00.
fromYesFirst day, inclusive, as YYYY-MM-DD in Paris time, e.g. 2026-10-01.
stepNoday: temperature and sky per day (at most 400 days). month: temperature only.day

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and openWorldHint, and the description adds real context beyond them: precision (0.1 °C daily, whole °C monthly), the fact that the sky is one coarse label per day, and Paris-time day boundaries matching the consumption tools. It stops short of describing row/field structure or any auth or rate-limit behavior, so it is strong but not exhaustive.

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?

Front-loaded with what the tool returns, then use case, then limitations and alignment. The middle sentence about heating demand, cooling, and sunny days is explanatory padding that could be trimmed without losing call-relevant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the burden of explaining returned content, and it does reasonably well by naming temperature and sky granularity per step. It does not commit to the shape of the returned rows, which leaves some uncertainty for a data-reading tool.

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%, so the parameters are already fully documented, including the day/month enum, the 400-day cap, and the yesterday default. The description restates the day-vs-month distinction and adds precision figures, but those describe output values rather than adding parameter meaning, so the baseline 3 applies.

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?

States a specific resource (the home's weather station as Suivi Conso shows it) and the exact content returned: average outdoor temperature per day plus a sky label, or temperature per month. It also distinguishes itself from an external weather archive, so the agent knows this is not a general weather API.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly frames the use case ('read consumption against the weather') and names the alignment with edf_get_electricity_consumption and edf_get_gas_consumption. It also gives an exclusion ('for sunshine hours or solar radiation use an external weather archive'), though it does not spell out when to skip this tool entirely.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.