get_mistake_audit

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 discloses that it aggregates tags and derives frequency, financial damage, and win-rate-when-mistake-occurs, which is meaningful behavioral context. It does not state the return format, whether an empty result means no mistakes, or any pagination/limit behavior, leaving gaps for a zero-annotation tool.

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?

Purpose is front-loaded in the first sentence, followed by the metrics computed, then example questions. Every element earns its place, though the four examples are somewhat redundant with the opening enumeration.

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 an aggregation tool with three simple filter parameters, no annotations, and no output schema, the description covers what is computed and when to reach for it. It could be slightly stronger by noting the read-only nature and the shape/granularity of the aggregation output.

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%, so all three parameters (account, date_from, date_to) are already documented in the schema. The description adds no syntax or format detail beyond that, so the baseline of 3 applies.

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?

Specific verb+resource: it audits recurring execution mistakes and enumerates the concrete mistake categories (Overleveraging, FOMO, Revenge Trading, etc.). It also states the computed outputs (frequency, total financial damage, win rate), which clearly distinguishes it from sibling stats tools like get_emotion_pnl or get_edge_by_market.

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 example questions give clear situational context (auditing discipline, cost of a specific mistake, filtering by account or last 30 days), which routes the agent to the right scenarios. However, it names no explicit alternative or when-not-to-use condition against siblings like get_emotion_pnl, so it stops short of full routing guidance.

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