loan_calculatorMonthly payment, total repayment and interest for an amortising loan (annuity formula). Runs on smart-tools.xyz.
| Name | Required | Description | Default |
|---|---|---|---|
| years | Yes | Term in years | |
| locale | No | Language for the source_url link (default en) | |
| principal | Yes | Loan amount | |
| annual_rate | Yes | Annual interest rate, percent |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It states the outputs but does not explicitly mention that it is a read-only calculation, does not describe return formatting, rounding, or handling of invalid inputs. The mention of 'Runs on smart-tools.xyz' provides provenance but not behavioral detail.
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 two sentences and front-loads the core functionality. The second sentence about smart-tools.xyz is arguably unnecessary but not distracting. Overall it is concise and readable.
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 simple calculator with complete schema coverage and no output schema, the description sufficiently lists the computed results. It does not explain return structure or edge cases, but the tool's complexity is low, and the basics are covered.
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 schema already documents all parameters. The description adds context by naming the annuity formula and output values, which clarifies the purpose of the parameters, but it does not add any syntax or format details beyond the schema.
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 computes monthly payment, total repayment, and interest for an amortising loan using the annuity formula. This is specific and unambiguous, but it does not explicitly differentiate from sibling tools like mortgage_calculator.
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?
Usage is implied by the phrase 'for an amortising loan (annuity formula),' indicating when the tool is appropriate. However, there is no explicit guidance on how to choose between this and similar calculators, nor any exclusions or alternative tool references.
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.
Most tools are clearly distinct, but there are notable overlaps. 'hash_generator' and 'file_hash' effectively do the same thing (SHA hashing of text), and several CSV/TSV converters (csv_to_json, csv_to_sql, table_to_csv, markdown_table) have similar input handling, though outputs differ. The sheer number of converters is clear, but these near-duplicates create some ambiguity.
Names follow a generally consistent pattern: lowercase with underscores, often ending in 'converter', 'calculator', or 'generator'. A few names like 'base64', 'lorem_ipsum', and 'transliteration' deviate from the suffix pattern, but the naming style is uniform and predictable overall.
With 73 tools, this server is far over the typical well-scoped range. While the broad utility-toolkit purpose might justify a larger set, 73 is unwieldy and pushes well into 'too many' territory, making it harder for an agent to select the right tool efficiently.
The server covers a remarkably wide range of common utilities: unit conversions, calculators, text transformations, development helpers (JSON, regex, JWT, subnet), and generators. Minor gaps exist (e.g., currency converter, time zone converter), but for a general-purpose toolkit, it is largely comprehensive.