abstract_case

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

With no annotations, the description carries full behavioral burden. It discloses that truncated is always False, explains drop_nan_attrs behavior and its token-saving impact, and references the backend library. However, it does not mention side effects or permissions, but for a read-like operation this is reasonable.

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?

Four focused sentences with clear formatting (uses code blocks) and no redundant information. Every sentence adds meaningful detail, making it concise and well-structured.

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 output schema exists for return values, the description covers purpose, two key parameters, and a behavioral note. Missing include_event_attributes explanation is the only gap, but overall it provides sufficient context for selecting and invoking the tool.

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%, so description must compensate. It explains case_id (must match column value) and drop_nan_attrs (default True, new feature, effect on output). However, log_id and include_event_attributes are unexplained, leaving gaps.

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 'Describe one case as a natural-language walkthrough of its events,' specifying the verb and resource. It distinguishes from sibling abstract_* tools by focusing on a single case, unlike abstract_variants or abstract_log_attributes.

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 a single case but does not explicitly provide when-to-use or when-not-to-use compared to alternatives. No exclusions or conditions are mentioned, leaving the agent to infer context.

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