cagrCompound annual growth rate — the honest average that volatile returns hide behind. CAGR between a start and end value over a period, plus the reverse projection — and why CAGR beats "average return" for judging any investment or revenue history.
| Name | Required | Description | Default |
|---|---|---|---|
| years | No | Years | |
| endValue | No | Ending value | |
| startValue | No | Starting value |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true, so no destructive effects. The description adds context about CAGR's advantage over average return and mentions reverse projection, but does not disclose additional behaviors beyond what annotations already convey. It is adequate but not exceptional.
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 a single sentence that is mostly informative but includes some editorial content ('the honest average that volatile returns hide behind'). While not lengthy, it could be more concise and focused on factual functionality.
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 description covers the main purpose (CAGR calculation and reverse projection) but lacks detail on what 'reverse projection' entails. Given no output schema and fully documented parameters, the description is moderately complete but could be more explicit about use cases and outputs.
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 fully documents each parameter with descriptions and defaults. The tool description provides a high-level overview but does not add parameter-specific meaning beyond what the schema already provides.
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 computes CAGR between start and end values over a period, and includes reverse projection. It identifies the specific financial metric (CAGR) and contrasts it with 'average return,' but does not explicitly distinguish from sibling tools like 'compound_growth' or 'roi.'
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?
There is no explicit guidance on when to use this tool versus alternatives. The description implies it is for CAGR calculations, but does not provide when-to-use or when-not-to-use criteria, nor does it reference sibling 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 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.