List Study Decisions

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds some content context by specifying that the read returns analyst decisions and agent recommendations, but it does not disclose ordering, scoping behavior, or other runtime caveats. Given the annotations, this is adequate but not rich.

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 a single eight-word sentence with no filler. It front-loads the operation and identifies the key content types, making it appropriately sized for a simple read tool.

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?

The tool is low-complexity: one required parameter, strong safety annotations, and an output schema is present, so return value details need not be in the description. The main gap is a lack of explicit differentiation from sibling decision-related tools, but the description and schema together give an agent enough to invoke the tool correctly.

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 0%, so the description must compensate by explaining study_id, but it never does. The phrase 'study decisions' weakly implies the study scoping, and the parameter name is self-explanatory, but no actual parameter semantics are added beyond the input schema. With low coverage, this compensation is required and missing.

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 identifies a read operation ('Read') over a specific resource ('analyst decisions and agent recommendations'), which goes beyond the title by naming two content types. An agent can distinguish it from list_studies and similar list tools. It does not explicitly contrast with siblings, so it falls just short of a 5.

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?

There is no guidance on when to use this tool versus alternatives. The sibling list includes recommend_study_run, get_study_validation_resolutions, and compare_study_runs, but the description does not explain why list_study_decisions is the appropriate choice or when another tool would be better. This is effectively no usage guidance.

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