state_why

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

No annotations are provided, so the description carries the burden of explaining behavior. It conveys that the tool performs causal analysis and indicates the content of the explanation (events, daily rhythm, coupling), but it does not state side effects, output format, or whether invocation is non-mutating.

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?

Two concise sentences carry all essential information: the first states the tool's core function and examples, the second states its intended use cases. There is no redundancy or filler.

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 zero-parameter diagnostic tool, the description is largely complete: it explains what the tool does and what kind of causal dimensions it reports. The main gap is the lack of explicit return format or side-effect behavior, but the low complexity makes that a minor omission.

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?

There are zero parameters, so the schema needs no semantic enrichment and the baseline is 4. The description adds conceptual context by naming the analyzed causal dimensions, which helps an agent understand what the tool considers even though no inputs are accepted.

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 and resource: it traces the current state back to its causes (Ereignisse, Tagesrhythmus, Kopplung). It clearly identifies the tool as a causal dagnostic operation, which differentiates it from state_read or state_history, though it does not name alternatives explicitly.

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 phrase 'Für Fehlersuche und Nachvollziehbarkeit' explicitly states when to use the tool: for debugging and traceability. It provides clear usage context, though it does not give exclusions or explicitly point to sibling alternatives.

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