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

Annotations already declare readOnlyHint=true and openWorldHint=true, so no contradiction. The description adds meaningful behavioral context beyond annotations: it reveals that the result includes the return model, number of paths, and income assumptions, and explicitly states that a zero Social Security value is reported as an explicit assumption rather than hidden. This gives the agent insight into the tool's output behavior without relying on an 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?

Three sentences with no filler. The first sentence states the core function, the second lists outputs, the third gives the tax caveat and alternative. All information is front-loaded and every clause earns its place.

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 tool with 13 parameters but no output schema, the description is remarkably complete: it states what the tool returns (success rate, distribution, assumptions, return model, path count, income assumptions) and highlights the tax limitation. It also references list_return_assumption_sources for the returns_source parameter, guiding the agent to the appropriate sibling. The annotations cover read-only and open-world behavior, leaving no critical gaps for correct invocation.

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 100%, so the parameters are already fully documented individually. The description adds contextual value by clarifying that tax is not modelled, which affects how annual_spending and other parameters should be interpreted, and by noting that income assumptions are always reported (even when zero). However, much of this is already present in the schema descriptions (e.g., 'before income tax'), so the marginal value is modest, matching the baseline of 3.

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 opens with a specific verb-resource pair ('Runs a Monte Carlo retirement projection on the QuantCalc engine') and lists concrete outputs (success rate, ending-portfolio distribution, assumptions). It explicitly differentiates itself from the tax-aware sibling by stating it does not model income tax, so an agent can tell it apart from run_tax_aware_projection without inspecting schemas.

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 description gives a clear context: it runs the standard engine and explicitly states when not to use it ('Income tax is not modelled by this tool') and names the alternative (run_tax_aware_projection). It does not mention other siblings like compare_return_assumptions or explain_methodology, but these are obviously different in purpose, so the guidance is adequate without being exhaustive.

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