correct

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

No annotations are provided, so the description carries full burden. It mentions 'high-priority' and 'always win' but does not disclose side effects, required permissions, reversibility, or behavior on duplicate idempotency keys.

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 sentences, no redundant words, every part adds value. Front-loaded with purpose.

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?

No output schema exists, and the description lacks return value details, error scenarios, or confirmation of success. For a mutation tool with 6 parameters (including required idempotency_key), more context is needed for correct invocation.

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 baseline is 3. The description adds minimal context beyond the schema (e.g., highlights idempotency_key as replay-safe). It does not explain all parameters in more depth, but schema itself is sufficient.

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 states the tool creates a high-priority correction observation to override AI-extracted fields. This verb-resource pairing is specific and distinguishes it from sibling tools like 'create_interpretation' or 'store' that may have overlapping but distinct purposes.

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 implies usage for overriding AI-extracted fields and notes that corrections win in snapshot computation, but does not explicitly state when to use this tool vs alternatives (e.g., 'create_interpretation' for non-correction observations) or provide exclusion criteria.

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