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get_cheapest_window

Find the cheapest N contiguous hours to run a flexible load (EV charging, battery, batch job) before a deadline. Calls the API cheapest endpoint, then refines using day-ahead hourly prices so the window respects the given UTC deadline (HH:MM interpreted as UTC — if that moment already passed today, tomorrow is used).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zoneYesBidding zone code (e.g. DE for Germany, FR for France).
hoursYesNumber of consecutive full hours to schedule (1–24), e.g. 4 for a 4-hour charge block.
beforeYesDeadline as HH:MM in UTC (23:59 style). The chosen window lies entirely on or before this instant on the UTC calendar day used (rolls to next day if already past).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses a non-obvious behavior: 'Calls the API cheapest endpoint, then refines using day-ahead hourly prices so the window respects the given UTC deadline.' It also explains the deadline rollover logic ('if that moment already passed today, tomorrow is used'), which is critical for correct invocation. However, it does not disclose the return format or potential error conditions, so it's 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no fluff. The first sentence front-loads the core purpose, and the second provides necessary behavioral nuance about deadline handling. Every word earns its place, making it highly concise and well-structured.

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

Completeness3/5

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

The description covers the input semantics and algorithmic behavior well, but without an output schema it should clarify what the tool returns (e.g., the start time, the list of hours, or the total cost). This omission leaves a meaningful gap for an agent needing to interpret the result. Given the tool's moderate complexity and no output schema, the description is adequate but not fully complete.

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 baseline is 3. The description adds minimal semantic value beyond the schema: it repeats the 'contiguous' and 'deadline' concepts already present in the property descriptions. The schema already explains the UTC interpretation and rolling behavior, so the description's contribution is largely redundant, meeting but not exceeding the baseline.

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?

The description clearly states the tool's purpose: 'Find the cheapest N contiguous hours to run a flexible load (EV charging, battery, batch job) before a deadline.' It identifies the specific task, the target resource (flexible load scheduling), and distinguishes itself from sibling tools by emphasizing the deadline and contiguous-hour requirement.

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 description implies usage context: 'for a flexible load (EV charging, battery, batch job) before a deadline.' This gives clear guidance on when to use the tool, though it does not explicitly name alternatives or exclusions. The absence of direct sibling references is a minor gap, but the context is sufficient for an agent to determine applicability.

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

A4.2/5.0
Disambiguation4/5

Tools are mostly distinct with clear scopes; compare_* tools each target a different metric (carbon vs green vs price), and get_carbon_intensity vs get_global_carbon differ by temporal and geographic scope. Minor confusion possible between get_cheapest_window and get_optimal_schedule but descriptions clarify single vs multi-zone.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern using get_ or compare_, with no mixed conventions. Nouns are descriptive and predictable.

Tool Count5/5

15 tools is at the upper bound of the ideal range but each serves a distinct function for an energy API covering prices, carbon, scheduling, and market insights. No redundancy or bloat.

Completeness4/5

Covers price (current, day-ahead, history), carbon (current, global, comparative), green scores, scheduling optimization, weather, and market specifics. Missing historical carbon intensity for zones and renewable generation mix, but core workflows are supported.

Resources