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

Beyond the readOnlyHint and destructiveHint annotations, the description discloses a critical behavioral rule: driver r values are null unless the tier is T3, VOI figures and overlap-corrected variance contributions are never exposed, and the tool withholds exactly what the public page withholds. This is substantive behavioral context that annotations alone could not convey.

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 long but densely informative and well-structured: the core purpose is front-loaded, followed by the integrity caveat. It earns its length by documenting an important data-withholding rule, though it could be tightened slightly.

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 one required parameter, a rich output schema, and simple annotations, the description fully covers what the tool returns, where the id comes from, what is withheld, and the parity with the public page. Nothing essential for correct invocation is missing.

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 description coverage is 100%, and the single id parameter is already documented with an example and source ('from list_outcome_models'). The description repeats the source reference but adds little new meaning beyond the schema, so the baseline 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 uses a specific verb and resource ('Get one outcome's full driver table by id') and enumerates exactly what is returned: driver name, definition, CAMS dimension, lens role, evidence grade, study count, tier, uncited-effect drivers, and personas. It clearly differentiates itself from list_outcome_models by being the single-item fetch counterpart.

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 explicitly says the id comes from list_outcome_models, establishing the correct retrieval sequence and clarifying that this is the one-outcome lookup tool. It does not explicitly state when not to use it, but the purpose is clear enough that an agent can infer the alternative is the list tool.

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