Log Correction

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

With no annotations, the description carries the burden of transparency. It explicitly discloses the optional side effect of creating a knowledge override, which is the most important non-obvious behavior. It could add detail about persistence or response, but it covers the key trait.

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 concise sentences front-load the purpose and usage trigger, with no wasted words. Every clause adds value.

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?

Despite having 8 parameters and no output schema, the description adequately conveys purpose, side effects, and when to use the tool. It could mention return/confirmation behavior but is complete enough for a logging tool.

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%, and the description does not add parameter-level semantics beyond what the schema already provides. This matches the baseline of 3.

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 uses a specific verb and resource ('Record when a calculation, rate, or rule was found to be incorrect') and clearly distinguishes this tool from siblings like log_research or update_knowledge by focusing on corrections and optional knowledge overrides.

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?

It provides a clear usage trigger ('Use this whenever you find a mistake in the agent's output or when a user points out an error'), but does not name alternatives or when-not-to-use scenarios, stopping short of a 5.

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