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

Annotations already provide read-only/destructive/idempotent hints. The description goes beyond them by disclosing that one successful call consumes 1 Oracle credit and by adding a high-impact advisory limitation. It does not detail determinism or failure behavior, but the annotations lower the burden and the added cost/limitation context is valuable.

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 with no filler. It front-loads the core use case, then adds the critical limitation and credit cost, making every sentence informative and structurally efficient.

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 output schema exists and all parameters are documented, the description covers selection and invocation needs: input shapes, analysis types, usage boundary, and cost. It does not explain how to phrase objectives or context in depth, but the schema already provides examples and the description is otherwise complete enough for correct 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 input, context, and objective are already documented in the schema. The description restates input types and lists analysis objectives that are nearly identical to the schema's examples, adding only minimal new meaning like 'recommendations'. This satisfies the baseline but does not substantially augment the schema.

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 names a specific capability: structured analysis of text, JSON objects, record arrays, and numeric series, with concrete output types like data-quality checks, trends, anomalies, summaries, and recommendations. This makes the tool's purpose clear and distinct even without siblings.

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 explicitly states when to use the tool ('when an application or AI agent needs structured analysis') and gives a clear exclusion boundary ('Do not use it as a substitute for professional advice in high-impact decisions'). No sibling tools exist, so additional alternative routing is unnecessary.

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