emiLoan EMI, total interest, and the flat-rate trap that makes 10% cost like 18%. Equated Monthly Instalment for any loan — home, car, personal — with total interest over the tenure and the one warning every borrower comparing offers needs: flat rate and reducing-balance rate are not the same thing.
| Name | Required | Description | Default |
|---|---|---|---|
| rate | No | Interest rate (reducing balance) (%) | |
| months | No | Tenure (mo) | |
| principal | No | Loan amount |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, so the tool is safe. The description adds valuable behavioral context by warning about the flat-rate vs. reducing-balance rate difference, which helps agents interpret results.
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 somewhat verbose and casual, including an exclamation and a metaphor ('flat-rate trap'). It front-loads outputs but could be more concise.
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?
No output schema is provided, so the description should explain return values. It mentions total interest and a warning but does not fully describe the output format (e.g., monthly EMI, total payment). Adequate but not complete.
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% with descriptions for each parameter (rate, months, principal) and defaults. The description does not add significant meaning beyond the schema, but the flat-rate warning indirectly relates to the rate parameter.
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 it calculates Loan EMI, total interest, and includes a crucial warning about flat vs. reducing-balance rates. It distinguishes itself from sibling tools like loan_payment, mortgage, and car_loan by emphasizing the flat-rate trap.
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 says 'for any loan — home, car, personal' but does not explicitly state when to avoid this tool or provide direct alternatives. However, the sibling list implies other specialized calculators for specific loan types.
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 niche (e.g., specific country tax rules, loan types, or legal calculations), with detailed descriptions that clarify boundaries. However, the large number of tools (66) could cause some confusion for an agent trying to select the right one for a general query, especially when multiple tools relate to the same country.
Tool names follow a mostly predictable pattern: lowercase words separated by underscores, often starting with a country name (e.g., 'uk_stamp_duty_sdlt') or a topic (e.g., 'compound_growth'). There are minor deviations, such as abbreviations ('npv_irr', 'sip') and varying use of verbs, but overall the naming is clear and consistent.
At 66 tools, the server is unusually large and covers an extensive range of financial and legal calculators. While each tool justifies its existence, the count exceeds the typical well-scoped range (3–15), making the server feel bloated. A more modular design might improve coherence.
The tool set covers a wide array of domains: personal income taxes, property taxes, loan calculations, investment returns, and specific country regulations. Minor gaps exist (e.g., missing tools for corporate taxes, general retirement planning, or insurance), but the overall coverage is thorough and addresses many niche scenarios that general AI handles poorly.