calculate_us_retirement_account

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

No annotations are provided, so the description must disclose behavioral traits. It mentions what calculations are performed (e.g., employer match, tax savings) but does not address side effects, authenticity (e.g., data persistence), rate limits, or error handling. The tool is likely read-only, but this is not explicit.

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 concise (two sentences plus example queries) and well-structured: purpose first, then account-type details, then concrete examples. Every sentence adds value with no redundancy or fluff.

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?

Given no output schema, the description sufficiently explains what the tool does for both account types and includes example queries. It covers the key differentials (MAGI phase-out, 401k limits). It could be more complete by mentioning output format or limitations (e.g., 2026 limits only), but it is adequate for an agent to use correctly.

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?

The input schema has 100% coverage with descriptions for all 9 parameters. The description adds value by clarifying which parameters apply to which account type (e.g., salary and employer_match for traditional_401k; magi and filing_status for roth_ira) and providing usage examples. This goes beyond the schema's basic descriptions.

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?

Description clearly states the tool analyzes US retirement account contributions for 2026, specifying two account types (traditional_401k, roth_ira) and associated calculations. Example queries further clarify purpose. However, the verb 'analyse' is slightly less precise than 'calculate', and the tool could be confused with generic retirement calculators if not for the sibling differentiation.

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 provides example queries that imply when to use the tool (e.g., tax savings, Roth IRA eligibility). However, it does not explicitly state when not to use it or mention alternatives among sibling tools (e.g., calculate_retirement, calculate_income_tax). Usage context is implied but not fully bounded.

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