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

Annotations already mark it read-only, idempotent, and non-destructive, so the description carries a low bar here and still adds substantial value. It discloses complex behavioral interactions: extra taxable Social Security, phased-out senior deduction, bracket ceilings from today's position, IRMAA with and without conversion, ACA subsidy cliff, and effect on the first RMD. There is no contradiction with the annotations.

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 dense but well-organized: the core question comes first, followed by a scoped list of additional outputs. It is one long sentence rather than bullets, but every clause contributes information and there is no 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?

For a complex 11-parameter tool with no output schema, it enumerates the main return dimensions: cost, bracket fit, IRMAA, ACA cliff, and first RMD. It does not spell out the exact output shape or state tax-law assumptions beyond '2026,' but the schema covers parameter-level details adequately.

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 the baseline is 3, but the description adds real meaning beyond the schema. The phrase 'on top of everything else that year' tells callers to pass baseline income separately, and listed outputs tie social_security, ages, aca_marketplace, household_size, pretax_balance, and expected_return to why they matter.

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 calculation — the federal tax cost of a Roth conversion from a traditional IRA/401(k) in 2026 — and enumerates distinct outputs such as bracket headroom, IRMAA tier, ACA cliff, and first RMD impact. This clearly differentiates it from siblings like federal_tax_estimate and rmd_calculate.

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 context is clear: use this when you need the marginal 2026 federal cost of a conversion on top of an existing year, including Social Security, senior deduction, IRMAA, ACA, and RMD effects. It does not explicitly name alternatives or say when to prefer federal_tax_estimate or rmd_calculate, so it stops 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.