abstract_log_attributes

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

With no annotations provided, the description carries full burden. It states the output is a textual description and wraps a specific function, but does not disclose behavioral traits such as whether it is read-only, performance implications, error conditions, or effect on system state. The existence of an output schema is not mentioned.

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, front-loads the purpose, and contains no filler. Every sentence adds value: the first defines core functionality, the second hints at usage context and implementation. Appropriate length for the tool's complexity.

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 no annotations and an output schema that is not described, the description lacks completeness. It explains the core purpose and output nature, but misses parameter semantics, usage examples, and behavioral details. For a tool that helps understand slicing dimensions, more context would be beneficial.

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?

Input schema has 0% description coverage (no descriptions in schema properties). The description does not explain the purpose or allowed values of 'log_id' or 'max_len', failing to add any meaning beyond the schema. This is a critical gap for a 2-parameter tool.

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 a textual description of attribute distributions including value frequencies and quantiles. The verb 'describes' and resource 'attribute distributions' are specific. It distinguishes itself from sibling tools like abstract_variants or abstract_dfg by focusing on attribute-level summaries.

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 mentions it is 'useful for the LLM to understand what slicing dimensions exist in the log,' providing a vague use case. However, it gives no explicit guidance on when to use this tool versus alternative abstract_* tools, nor any when-not-to-use or prerequisite conditions.

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