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

With no annotations, the description carries the full burden and does so thoroughly. It explains the key conceptual distinction between 'data_silent' (voter source offline, not the voter's fault) and 'real decay' (voter scores no longer predictive), and enumerates all returned fields including the capped ±0.05 weight delta. This prevents misinterpretation and gives the agent a solid mental model of the output.

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 four dense, information-rich sentences with no filler. It front-loads the core purpose, then covers differentiation, call triggers, and return fields. Every sentence earns its place, and the structure flows logically from what to when to what-you-get.

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?

Despite having no output schema and no annotations, the description is complete for a zero-parameter analytical tool. It lists all key return fields, explains the critical data_silent vs real_decay semantics, and references the sibling tool for contrast. There is no significant missing context that would hinder correct invocation or result interpretation.

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 tool has zero parameters, so schema coverage is trivially 100% and the baseline is 4. The description adds context about the 12-voter ensemble and the per-voter granularity, which helps the agent understand what the no-parameter invocation returns, even though no parameter details are needed.

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's function: 'Per-voter IC drift breakdown' with a specific focus on identifying which of the 12 voters is decaying. It explicitly distinguishes itself from the blended-IC drift tool by contrasting 'IF the ensemble is decaying' versus 'WHICH voter', making the purpose unambiguous and well-differentiated from a key sibling.

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?

Provides explicit call triggers with real user phrasing ('which voter is the problem?', 'why did the model decay?', 'should we re-weight?') and explicitly names the alternative blended-IC drift tool for the ensemble-level IF question. This gives clear when-to-use and when-not-to-use guidance, going beyond generic context.

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