optimizer_cheapest_window
Flexible-load agents buy this endpoint to schedule a contiguous operating window at the lowest average electricity price.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| duration_hours | No |
Flexible-load agents buy this endpoint to schedule a contiguous operating window at the lowest average electricity price.
| Name | Required | Description | Default |
|---|---|---|---|
| duration_hours | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full burden. It states the core function but does not disclose whether the tool executes a purchase, returns a schedule, or has side effects. The phrase 'buy this endpoint' is ambiguous and not elaborated. Score 2.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence with no filler, front-loading the purpose. Score 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and an ambiguous 'buy' action, the description leaves critical gaps about behavior and return values. The simplicity of the schema does not compensate for the missing context. Score 2.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter (duration_hours) with no description in the schema, and the description does not mention it. The parameter name and constraints are self-evident, but the description adds no value in explaining how it relates to the window. Score 2.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the verb 'schedule' and identifies the resource as 'a contiguous operating window at the lowest average electricity price,' which clearly conveys the tool's function. However, it does not explicitly distinguish it from similar sibling tools like flexibility_window, so a 4 is appropriate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions the target user ('Flexible-load agents') and the context ('lowest average electricity price'), implying when to use it, but it does not provide explicit guidance on alternatives or exclusions. A score of 3 reflects that the usage is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Multiple tools overlap significantly: buy_dispatch_plan, flexibility_window, optimizer_cheapest_window, and energy_decision all help schedule or choose an energy window, while price_forecast, price_spot, and buy_market_brief provide pricing context. The paired GET-fallback tools (articles_id vs articles_id_post, demand_submit vs demand_submit_post, etc.) create further ambiguity.
Naming is inconsistent: some tools use a verb prefix (buy_, predict_, subscribe_), others start with a noun (price_, grid_, carbon_), and some have non-verb suffixes (_post, _quick). Related tools vary in style, e.g., price_forecast vs predict_negative_price and demand_submit vs demand_submit_post.
With 31 tools, the server feels heavy. While many are distinct paid endpoints, the high number—including near-duplicate variants—exceeds the 25-tool threshold for comfort and suggests an over-sized surface.
The energy domain is well covered: real-time and historical prices, forecasts, negative-price prediction, dispatch/flexibility optimization, CO2, renewables, load, subscriptions, and research. Minor gaps exist (e.g., historical CO2, user account handling), but core agent workflows are supported.