calculate_ai_margin

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses that losses are unbounded (not floored) and that values are directional. This goes beyond basic functionality and adds useful behavioral context. No contradiction with annotations.

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 paragraph that front-loads the primary action and lists key outputs. It is relatively concise with no redundant sentences, though it could be structured more clearly with separate sections. It earns its space.

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?

Given 9 parameters, no output schema, and no annotations, the description covers the core functionality well: what it calculates, important behaviors (unbounded loss), and that it's directional. It does not explain all parameter meanings but compensates with schema coverage. A bit more on output interpretation would improve completeness.

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 9 parameters have descriptions in the input schema (100% coverage), so the baseline is 3. The description mentions cost categories like 'token bill, card processing fees' but does not map them to specific parameter names or add meaning beyond the schema. It adds marginal value.

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's function: 'Turn an AI product's MRR into what the builder actually keeps' and lists specific outputs like break-even price and per-customer margin. It distinguishes from the sibling 'get_ai_tool_prices' by focusing on margin calculation rather than price lookups.

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 use for computing net margins and break-even metrics, and mentions it mirrors a known calculator. However, it does not explicitly state when to use this tool versus the sibling 'get_ai_tool_prices' or provide exclusions. The directional/non-advice note is helpful but not a usage guideline.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.