price_forecast
Energy-trading and flexible-load agents buy this endpoint to compare the next 24 hours of EPEX prices and volatility.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Energy-trading and flexible-load agents buy this endpoint to compare the next 24 hours of EPEX prices and volatility.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for disclosing behavior. It mentions 'buy' (suggesting a cost) but does not explain side effects, authentication needs, rate limits, or what the response actually contains. The agent is left guessing about the operational details beyond the stated comparison purpose.
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, compact sentence that front-loads the core purpose. It avoids unnecessary words, though the phrase 'buy this endpoint' could be simplified. Overall, it is concise and structured acceptably.
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 simple, no-parameter tool, the description gives the core purpose and target audience. However, with no output schema and no annotations, it should at least hint at the response format or any special conditions (e.g., subscription required). The description is minimal but not entirely inadequate, so a 3 is appropriate.
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 tool has zero parameters and the schema is an empty object (100% coverage). The description adds no parameter-specific information, but none is needed. Baseline for zero parameters is 4, and nothing in the description detracts from that.
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's function: comparing next 24 hours of EPEX prices and volatility. It names a specific resource (EPEX prices) and a distinct time window, which differentiates it from historical or spot-price siblings like history_prices and price_spot. The verb 'buy' is slightly odd but does not obscure the purpose.
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 usage for energy-trading and flexible-load agents, giving a clear target audience and context. However, it does not explicitly state when not to use this tool or mention any alternative tools, relying on the agent to infer from sibling names.
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.