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

Beyond the readOnly and closed-world annotations, the description adds important behavioral context: it returns labeled illustrative probabilities, never live inference, and requires no account token. It could say more about determinism or exact return shape, but for a simple demo this is strong disclosure.

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 three short sentences, front-loads the use case, then clarifies output behavior and pricing/auth context. Every sentence adds useful information without repetition.

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 one-parameter demo with annotations and no output schema, the description covers purpose, usage, auth needs, and the illustrative/non-live nature of results. The only minor gap is that the exact response shape is not described.

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 0%, so the description must carry parameter meaning. It does so by naming all four enum values and framing them as fixed examples of enterprise, finance, robotics, or handoff decisions, which gives useful semantic context beyond the bare enum.

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 states that this is a free demo/connection check returning fixed illustrative probabilities for four named decision domains. It clearly distinguishes itself from live inference and paid tools, though it does not call out specific sibling tools by name.

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 gives explicit use cases: a free connection/interface check or a fixed example across enterprise, finance, robotics, and handoff decisions. It also states when not to use it for real evaluation by directing users to a paid tool for their own input, but it does not name which sibling paid tool to choose.

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