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

Annotations already establish read-only, non-destructive, closed-world behavior, so the bar is lower; the description still adds real value by disclosing that every figure derives from the projection, that uncomputable figures are null with a stated reason rather than estimated, and that caveats should be read before quoting. What it does not cover is whether figures are cached/as-of when, or the response shape, though the null-with-reason rule does set expectations about the payload.

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?

Content is front-loaded: the payload enumeration comes first, then the interpretive framing, then the data-quality caveat. The middle clause is a long dense list, but every item corresponds to something the tool actually returns, so little is wasted.

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?

With no output schema, the description carries the burden of describing the return payload and does so by enumerating its components, plus it explains null semantics and the existence of caveats. For a zero-required-parameter aggregate read, an agent has enough to call it and to interpret the result responsibly.

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% and the sole optional parameter is documented in-schema with its default and a pointer to fin_periods. The description only echoes 'for a period' without adding format, default behavior, or what happens when the period is omitted, so it neither compensates nor detracts.

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 the resource and enumerates the concrete figures returned (expected income, spend, profit, break-even point, headroom, committed vs planned, nearest obligations), so an agent can tell this is the composite summary rather than a single-metric tool. It stops short of naming the overlapping siblings (fin_break_even, fin_upcoming_obligations, fin_cashflow) whose content is folded in here, leaving some ambiguity for a model choosing between the aggregate and its parts.

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?

'Answers where are we and where are we heading' implies the use case (a status/position question spanning a period) and 'check the caveats before quoting a number' implies a verification step. There is no explicit when-not guidance and no alternative tool is named, so routing against fin_break_even or fin_upcoming_obligations must be inferred from content overlap.

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