Schedules a deadline-bound compute, DePIN, battery or power-trading workload into an actionable German or European power window. Returns exact timing, expected EUR cost and savings, uncertainty, freshness, expiry and decision trace.
| Name | Required | Description | Default |
|---|---|---|---|
| deadline | Yes | ||
| power_kw | Yes | ||
| energy_kwh | Yes | ||
| constraints | No | ||
| risk_profile | Yes | ||
| opportunity_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states 'schedules' and lists return fields. It fails to disclose whether the tool is destructive, idempotent, or requires specific permissions. The behavioral impact of scheduling is not addressed beyond the immediate action.
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 two sentences long with no redundant words. It front-loads the core action and lists return values efficiently. However, it could be more structured by grouping elements or adding separation.
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?
Given the tool's complexity (6 parameters, nested constraints, no output schema), the description lacks crucial context on parameter relationships, preconditions, and validation rules. The return value list is helpful, but the overall completeness for agent decision-making is low.
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 coverage is 0%, and the description does not explain any of the six parameters (opportunity_id, power_kw, energy_kwh, deadline, risk_profile, constraints). 'Deadline-bound' weakly hints at the deadline parameter, but no concrete semantics are provided.
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 a specific verb 'schedules' and identifies the resource as 'deadline-bound workload into an actionable power window'. It clearly distinguishes from siblings like 'buy_market_brief' and 'find_energy_opportunity' by focusing on scheduling execution rather than buying briefs or finding opportunities.
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 scheduling workloads into power windows, but does not explicitly state when to use this tool versus alternatives. No guidance on prerequisites, exclusions, or post-scheduling steps is provided, making the context only inferred.
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.
