LLM spend: API vs subscription math

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

No annotations are provided, so the description carries the full burden. It adds 'Free, no auth' and details the three outputs (subscription tier, flat total, monthly savings). This goes beyond a minimal statement, though it does not cover error handling or edge cases. For a non-mutating calculator, this is adequate transparency.

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; the first packs essential functional info (input, output, fee), and the second adds 'Free, no auth.' No unnecessary words. It is front-loaded and immediately informative.

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?

With one parameter and no output schema, the description lists the three output components (subscription tier, flat total, monthly savings) but does not specify the exact response structure (e.g., JSON field names). However, the tool is simple enough that an agent can infer the return contents. The description is complete enough for invocation and basic interpretation.

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 0%, but the description explicitly defines the parameter's meaning: 'monthly OpenAI API bill in USD' maps directly to monthly_openai_bill_usd. It also clarifies the computation context. With only one parameter, this is sufficient to understand what value to provide.

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 what the tool does: given a monthly OpenAI API bill in USD, it returns the appropriate subscription tier, the flat total with the $129 fee, and monthly savings. The specific verb 'returns' and the resource 'savings calculator' distinguish it from sibling tools like proxyllm_list_models or proxyllm_usage, which serve different purposes.

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 specifies the input condition ('Given a monthly OpenAI API bill in USD') and implies the use case: comparing API spend against subscription costs via ProxyLLM Codex Hosted. Although it does not explicitly mention when not to use it or alternative tools, the sibling list shows no similar calculator, so the context is sufficient for an agent to decide when to invoke it.

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