get_regelleistung

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only names the data content and source but does not mention any behavioral traits such as data granularity, time zone, units, availability limitations, or response format. The claim 'Primary BESS revenue data source' hints at relevance but lacks operational details.

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 extremely concise, consisting of two short, information-dense sentences. It front-loads the source name and data type, then adds the BESS revenue context. Every word contributes value, with no fluff or repetition.

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 has only two parameters and no output schema, the description should provide more context about return values, units, time periods, or data aggregation. It only states 'prices and volumes' without specifying units (e.g., €/MW, MWh) or temporal coverage. For a data source critical to BESS revenue modeling, this is a significant gap.

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 input schema has 100% description coverage for both parameters: 'date' has format and default, and 'product' has an enum with detailed definitions for FCR, aFRR, and mFRR. The description adds no extra parameter semantics beyond the schema, so the baseline of 3 is appropriate.

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 FCR/aFRR/mFRR reserve tender prices and volumes from Regelleistung.net, naming the specific data domain. It distinguishes itself from sibling energy data tools by specifying the exact reserve products and the primary use case, though it lacks an explicit verb like 'retrieve' or 'fetch'.

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?

It mentions 'Primary BESS revenue data source,' which implies the tool is for battery energy storage system revenue analysis, offering some usage context. However, it does not explicitly state when to use this tool versus alternatives like get_flexibility_market or get_auction_results, nor does it provide any exclusions or alternative recommendations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.