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

Annotations already mark the tool read-only and idempotent; the description adds that it runs a month-by-month projection, applies optional account rules, and returns a link to a savingslast.com page with the same inputs. It also discloses that income is netted against spending and that a stress test changes return assumptions, which an agent would not learn from annotations or the schema alone.

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 front-load the core result and then layer optional behaviors and the external link. No sentence is filler; the description stays compact despite a 13-parameter tool.

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 calculator with no output schema, the description enumerates the key return values and groups the optional parameter families coherently, while the schema supplies per-parameter details. It does not state the underlying mortality/return assumptions or the exact format of the results, but what an agent needs to select and invoke the tool is present.

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 structured descriptions already carry most parameter meaning. The description adds value by grouping optional parameters into behaviors—account rules (early-withdrawal penalty, RMDs), Social Security/pension netting, stress test, and spending shape—making the optional fields' joint purpose easier to grasp.

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 concrete calculation ('Month-by-month drawdown of a retirement balance') and enumerates distinctive outputs: years until exhausted, run-out age, survival odds, and sustainable withdrawal ceilings for 20/25/30 years. This makes the tool's role clear even though it does not explicitly contrast it with sibling tools like sustainable_withdrawal.

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 conveys when to use it—for retirement drawdown projections with optional account rules, Social Security/pension offsets, stress tests, and spending shapes—and these feature cues imply use cases. However, it never names an alternative tool or states when not to use this one, so differentiation from siblings is left to inference.

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