Este proyecto implementa un **sistema completo de análisis y predicción del consumo energético doméstico** utilizando técnicas de machine learning y
Este proyecto implementa un sistema completo de análisis y predicción del consumo energético doméstico utilizando técnicas de machine learning y explicabilidad de modelos (SHAP). El sistema permite realizar predicciones precisas. Exposed over MCP by the tfm mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
You will need one environment variable: ESIOS_API_TOKEN. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
Among the developer tooling options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. It is maintained by Davad122; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against TFM's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
{
"mcpServers": {
"mcp-david-TFM": {
"command": "uv",
"args": [
"--directory",
"RUTA_DEL_PROYECTO",
"run",
"-m",
"davidElectric"
],
"env": {
"ESIOS_API_TOKEN": "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| ESIOS_API_TOKEN | Credential the server authenticates with. | Yes |
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