Estimate monthly & fortnightly principal-and-interest repayments and total interest for a home/personal loan scenario. Indicative only — not a quote.
Args:
principal: Loan amount in AUD (e.g. 500000).
annual_rate_pct: Annual interest rate as a percentage (e.g. 6.5).
term_years: Loan term in years (e.g. 30).
| Name | Required | Description | Default |
|---|---|---|---|
| principal | Yes | ||
| term_years | Yes | ||
| annual_rate_pct | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must bear the full burden. It discloses the result is indicative only, which is relevant, but does not mention any side effects, speed, or error handling. This is minimal but acceptable for a calculation tool.
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 with two sentences plus a docstring. It front-loads the purpose and parameters efficiently. However, the parameter descriptions could be integrated into the JSON schema for better structure.
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?
The tool has no output schema, and the description does not detail the output structure beyond naming the computed values (monthly/fortnightly repayments, total interest). For a simple calculation, this is nearly sufficient but leaves some ambiguity about the response format.
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?
All three parameters have clear definitions and examples in the description (e.g., principal in AUD, annual_rate_pct as percentage). Since schema description coverage is 0%, the description fully compensates and provides meaning beyond the schema types.
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 clearly states the tool estimates monthly and fortnightly repayments and total interest for a loan scenario. It distinguishes from sibling tools like estimate_borrowing_power and compare_loan_types by focusing on repayment calculation.
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 lacks guidance on when to use this tool versus alternatives. It only mentions it's indicative and not a quote, but does not specify prerequisites, context, or exclude other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct aspect of the mortgage process: repayment calculation, Costco eligibility, loan type comparison, enquiry submission, borrowing power estimation, and rate guidance. There is no functional overlap.
All tool names follow a consistent verb_noun pattern (e.g., calculate_repayments, check_costco_eligibility, compare_loan_types), using lowercase with underscores.
With 6 tools, the set is well-scoped for a mortgage advisory server, covering key pre-application steps without being overly large or too sparse.
The tools cover the core pre-application workflow: calculation, borrowing power, rate guidance, comparison, eligibility, and enquiry. A minor gap is the lack of tools for existing loan management or detailed offer comparison.