pricing_marginMargin vs markup, and what a discount really costs in volume. Computes gross margin and markup from cost and price — two numbers people constantly confuse — and shows the brutal volume math behind discounting at your margin.
| Name | Required | Description | Default |
|---|---|---|---|
| cost | No | Unit cost | |
| price | No | Selling price | |
| discount | No | Planned discount (%) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true, and the description adds context about computing margin, markup, and volume math. It does not contradict annotations and provides useful behavioral detail beyond the read-only hint.
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, front-loaded with a catchy title-like phrase, then concise functional details. No wasted words.
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 calculation tool with read-only hints and no output schema, the description covers the main outputs (margin, markup, discount volume impact). It could be slightly more explicit about the return format, but it is adequate given the tool's simplicity.
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 descriptions cover all three parameters (cost, price, discount) with basic names. The description adds meaning by explaining how cost and price are used for margin/markup and discount for volume math, thus adding value 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 gross margin and markup from cost and price, and additionally shows the volume impact of discounting. It distinguishes itself from sibling tools like break_even or unit_economics by focusing specifically on margin vs markup and discount math.
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 implies usage for margin/markup calculations and discount analysis, but does not explicitly exclude alternatives or provide when-not-to-use guidance. Given the context of many sibling financial tools, some exclusion would be helpful but the current clarity is strong.
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.