mistake_kinds

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds non-obvious behavioral context: the output is an aggregate over all past entries and is sorted by frequency, which matters for interpretation. No return-format detail, but an output schema exists to carry that.

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?

Two short sentences, the observable behavior leads and the usage rationale follows. Nothing is redundant with the schema or annotations.

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?

With zero params, read-only annotations, and an output schema defining the return shape, the description covers what an agent needs to decide and call. Only the sibling routing is left implicit.

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?

Zero parameters, so there is no parameter semantics to explain — baseline applies. The description correctly avoids inventing inputs and frames the tool as a no-argument enumeration.

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 resource (mistake labels used so far) and its ordering (most frequent first), which an agent can distinguish from list_mistakes (records) and mistake_patterns (analysis). However, it never names those siblings, so differentiation is inferred rather than explicit.

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?

Gives a concrete usage context — call this before logging a new mistake to reuse existing labels for consistent aggregation. It lacks explicit when-not-to-use or named alternatives, but the intended workflow is clear.

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