Day-ahead wind + solar generation forecasts: the TSO's official forecast AND Voltcast's own model (volt-res-1) with q10-q90 bands, beside realized generation, with head-to-head verification (who beats whom, disclosed). Pro/Scale.
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | ISO 8601 end | |
| from | No | ISO 8601 start | |
| zone | Yes | Bidding zone code, e.g. 'DE-LU' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by disclosing the return contents: TSO and Voltcast forecasts, q10-q90 bands, realized generation, and head-to-head verification. It goes beyond a simple 'get data' and explains the tool's comparative feature, though it omits error handling or rate limits.
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?
The description is a single dense sentence that front-loads the main purpose and packs in relevant details (model name, bands, realized data, verification). The trailing 'Pro/Scale' is somewhat unstructured but does not waste significant space. It is concise given the amount of information conveyed.
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 output schema, the description explains the key return components (forecasts, bands, realized generation, comparison). It is complete enough for an agent to understand what will be received, though it lacks units or temporal resolution specifics. The sibling context is not fully addressed, but overall the description is robust.
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?
Schema description coverage is 100% for the three parameters, so the baseline is 3. The description adds no additional meaning about the zone, from, or to parameters, but the schema already provides sufficient semantic details (ISO 8601, bidding zone examples).
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 clearly states the tool provides day-ahead wind and solar generation forecasts, including both the TSO official forecast and Voltcast's own model, distinguished from siblings like get_prices or get_real_time_prices by its specific renewable generation focus. It names a concrete resource and deliverable.
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 implies use for renewable generation forecasts but does not explicitly state when to prefer this tool over siblings such as get_forecast or get_prices. No exclusions or alternative tool names are provided, leaving the selection to inference.
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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Each tool addresses a distinct data domain or action: zone discovery, price retrieval (day-ahead vs real-time), forecasting, carbon intensity, renewables generation, and cost-optimized scheduling. Even closely related tools like get_prices and get_forecast are clearly separated by their descriptions.
The get_ prefix is used consistently for data retrieval, and list_zones follows the expected list_ convention. The only outlier is cheapest_window, which would fit the pattern better as get_cheapest_window, but the inconsistency is minor.
Seven tools is well-scoped for an electricity market data API. Each endpoint represents a meaningful capability without redundant or filler tools, and the count supports both simple lookups and more advanced optimization workflows.
The toolset covers zone discovery, day-ahead and real-time prices, probabilistic forecasts, carbon intensity, renewables generation, and load scheduling. Minor gaps exist around historical data and broader market detail, but core workflows are well supported.