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

The description discloses important behavioral details beyond the annotations, such as the auto-calculation of business value from a baseline if one exists, and provides a concrete ROI example. Annotations are all false hints, but the description adds meaningful context about the tool's behavior without contradicting them.

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 concise and well-structured, with a clear statement of purpose, a useful clarification about auto-calculation, and a concrete example. Every sentence adds value, and the example effectively illustrates the tool's use.

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?

Given the tool has 9 parameters, an output schema, and moderate complexity, the description covers the core concept well. It explains the main purpose, the auto-calculation behavior, and gives a practical example. It does not detail every parameter, but the schema fully covers that, so the description is sufficiently complete.

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 the schema already documents all parameters. The description adds value by explaining the relationship between process_name and value_generated_usd (auto-calculation from baseline) and by giving a concrete example of ai_cost_usd, value_generated_usd, and ROI. This enriches the schema without repeating it.

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 'Log' and identifies the resource as 'a business decision made by an AI agent,' clearly distinguishing this tool from sibling tools like thinkneo_log_event or thinkneo_log_risk_avoidance. It also highlights the tracking of AI cost and business value, which further clarifies its unique purpose.

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 gives clear context for when to use this tool: whenever an AI agent makes a business decision that should be logged with cost and value implications. It provides an illustrative example but does not explicitly state when not to use it or mention alternatives, so it falls just 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.