coaching_coaching_iterate_judgment

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

No annotations are provided, so the description carries the full burden. It does disclose the core mechanism (LLM-driven priority adjustment) and the return shape, but omits whether the rule mutation persists, whether it is reversible, or any permission/auth requirements for changing investment rules.

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?

Front-loaded purpose, then usage trigger, then Args, then Returns. Efficient overall, though the opening prose and the Args section restate the same two inputs, adding minor redundancy.

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?

For a two-parameter tool with no output schema and no annotations, the description covers purpose, trigger context, both parameters with examples, and the response shape. The main gap is behavioral: persistence and reversibility of the rule mutation are unstated.

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% (properties have only titles), so the description must compensate, and it does: it supplies a concrete example for each parameter (wrong_variable: uncontrolled variable like 'gold and dollar rising together'; correct_result: what actually happened). This meaningfully disambiguates both string inputs beyond the bare titles.

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?

States a specific verb+resource: updating variable priority in investment research rules after a wrong judgment. The mechanism (LLM analyzes and suggests which variable to raise/lower) is concrete. It does not, however, distinguish itself from nearby siblings such as coaching_coaching_suggest_thresholds or the other coaching_ tools, leaving the agent to infer the boundary.

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?

Explicitly states when to use it: after the user makes a wrong judgment, feeding the wrong variable and the actual correct result. That is a clear triggering condition. It offers no exclusions or named alternatives, so the agent cannot tell when NOT to use it versus the other coaching_ tools.

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