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

With no annotations, the description carries the burden of disclosing behavior. It mentions setting actual_outcome and computing delta, but does not clarify if the operation is destructive, reversible, or requires specific permissions. More detail on side effects would improve transparency.

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?

The description is two sentences, front-loaded with the core action, and contains no redundant information. Every sentence contributes meaning.

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 simple tool with no output schema and 3 fully described parameters, the description covers the purpose, computation, and long-term benefit. It is missing return value details and error conditions, but overall it is sufficiently complete for typical use.

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 coverage is 100%, so the baseline is 3. The description adds meaning by framing actual_outcome as 'what actually happened' and explaining the computed delta, which goes beyond the schema's description of 'observed real-world outcome value.' Outcome_notes is not elaborated, but the context provided is valuable.

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's purpose: to record the actual outcome after a decision, compute the delta, and close the feedback loop. It uses specific verbs and resources, and distinguishes itself from sibling tools like log_decision by focusing on post-decision recording.

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 indicates when to use the tool ('after a decision was made') and implies it is part of a feedback loop. However, it does not explicitly state when not to use it or provide alternatives, leaving some ambiguity.

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