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

With no annotations, the description must carry the behavioral transparency burden. It discloses the core calculation logic (steps, tokens, model pricing, success rate) and the intended transformation. However, it omits details such as whether pricing data is built-in, how cacheHitRatePct is factored in, or what the output format will be, leaving some behavioral ambiguity.

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 front-loads the tool name and then provides a concise explanation of what it calculates. It is efficient, though it repeats the title 'AI Agent Cost Calculator' and could be slightly tighter. No unnecessary filler.

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?

This tool has 7 required parameters, no output schema, and no annotations, so the description needs to explain the full context. It gives a useful conceptual overview but does not specify the return value (e.g., a currency amount, daily cost, or breakdown), how cache hits affect the result, or any assumptions about model pricing. The description is not complete enough for an agent to fully understand the tool's behavior without additional inference.

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%, so the description must compensate for the 7 parameters. It covers several conceptually: 'steps' (stepsPerRun), 'tokens' (inputTokensPerStep/outputTokensPerStep), 'model pricing' (modelId), and 'success rate' (successRatePct), but it does not mention runsPerDay or cacheHitRatePct. It adds high-level context but not full per-parameter semantics.

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 explicitly states the tool's purpose: to calculate the true cost per successful task for an AI agent, incorporating steps, tokens, model pricing, and success rate. It uses a specific verb ('See') and resource ('AI agent cost per successful task'), and the mention of 'successful task' distinguishes it from sibling tools like llm_api_cost_calculator or fine_tuning_cost_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?

The description implies the usage context by explaining that it converts 'dashboard cost per run into the true unit cost,' which suggests it is meant for scenarios where per-run costs and success rates are known. However, it does not explicitly state when to use this tool over alternatives, nor does it provide any exclusions or mention of sibling tools.

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