mcp.xynaptic

energy-demand

Xynaptic Energy Demand FR — live French electricity demand (RTE éCO2mix official): consumption MW, day-ahead forecast, CO2 rate g/kWh, per-fuel generation mix. Quarter-hourly freshness. GET (?date=YYYY-MM-DD, default today). Usage: Use for live French electricity consumption, forecast, CO2 rate and generation mix (RTE official). [price: $0.020 per call, x402/USDC]

Input Schema

Name	Required	Description	Default
`params`	Yes	query parameters

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedOct 3, 2026
- addedInput schema / properties / params / required
```
Added value: +[]
```
- changedInput schema / required
```
Previous value: -[]New value: +[
+  "params"
+]
```

Changed4 schema fields changedOct 2, 2026

removedInput schema / properties / body

Removed value: -{
-  "description": "JSON body for POST endpoints (e.g. insurance, ai-chat, production-risk)",
-  "type": "object"
-}

changedInput schema / properties / params / description

Previous value: -"query parameters, e.g. {city: 'Paris', type: 'Appartement'}"New value: +"query parameters"

addedInput schema / properties / params / properties

Added value: +{
+  "co2_g_kwh": {
+    "description": "example: 47",
+    "type": "number"
+  },
+  "consumption_mw": {
+    "description": "example: 35829",
+    "type": "number"
+  },
+  "date": {
+    "description": "example: \"2026-10-02\"",
+    "type": "string"
+  },
+  "forecast_mw": {
+    "description": "example: 47000",
+    "type": "number"
+  },
+  "nuclear_mw": {
+    "description": "example: 37618",
+    "type": "number"
+  }
+}

addedInput schema / required
```
Added value: +[]
```

AddedOct 2, 2026

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations, the description carries the full load and discloses meaningful traits beyond the schema: official RTE source, quarter-hourly freshness, GET semantics, and pay-per-call pricing ($0.020 via x402/USDC). It does not state rate limits, error behavior, or geographic/historical coverage boundaries, but the behavioral picture is solid for a read-only data fetch.

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-loads the resource and data source, then the return fields, freshness, request format, usage, and price in a compact run of sentences. It is slightly repetitive, restating 'RTE official' and re-listing the output fields in the Usage sentence, but no sentence is expendable.

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 annotations and no output schema, the description adequately covers source, freshness, output fields, request format/default, and pricing. Missing only edge details like rate limits, error handling, or whether non-today dates are backfilled, which are minor for a read-only endpoint.

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 coverage is 100%, so the baseline is 3, and the description does add the date format (YYYY-MM-DD) and default (today). However, the nested schema lists response-style fields (consumption_mw, forecast_mw, co2_g_kwh, nuclear_mw) as input properties, and the description never clarifies these should not be passed, leaving a real semantic ambiguity unresolved.

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?

Names a specific resource (live French electricity demand from RTE éCO2mix) and enumerates exactly what it returns: consumption MW, day-ahead forecast, CO2 rate, and generation mix. This is distinguishable from siblings like energy-price and energy-interconnections without opening any schema.

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?

The explicit 'Usage: Use for live French electricity consumption, forecast, CO2 rate and generation mix' sentence tells the agent when to reach for this tool, and the 'live/quarter-hourly' framing implies it is for current rather than historical data. It stops short of naming alternatives such as energy-price for market pricing, so there is no explicit when-not guidance.

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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