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

Annotations already declare this as a safe, read-only, idempotent computation, so the safety bar is low. The description adds genuine value by disclosing the concrete outputs produced (per-period rows, doubling time, time-to-target), which matters since there is no output schema.

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?

A single tight sentence that front-loads the operation and model, then enumerates outputs with zero filler.

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 usefully explains the return shape, and the computation model is unambiguous. The main residual gap is routing guidance relative to sibling growth/forecast tools, which the description never addresses.

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 only 40%, but the description reinforces the compound-per-period interpretation of growth_rate_pct and the optional target_value, which adds real meaning over the schema. It does not cover period_type semantics (month/quarter/year) or the periods upper bound, so it partially compensates for the gap.

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?

States a specific verb (projects forward) plus the exact model (compound growth per period), and names the concrete outputs: per-period values, doubling time, target horizon. This clearly distinguishes it from siblings like growth_rate (single rate), ytd_growth, and period_change.

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?

Usage is only implied by the described outputs; there is no explicit statement of when to pick this over growth_rate, period_change, or ppc_forecast, and no mention of prerequisites or limits (e.g., the 120-period cap). An agent must infer the choice from the math being described.

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