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

With no annotations provided, the description carries the full burden of disclosure. It explicitly explains the stateless nature of the API, the token cost formula (size times number of turns left), and the return values. It stops short of explicitly stating it has no side effects, but the nature of a pricing calculation implies a non-destructive operation. The description is transparent about the model and outputs.

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 tightly structured: it states the purpose, explains the underlying model, lists the return values, and gives a concrete use case—all in four sentences. No wasted words. The critical information is front-loaded, and every sentence contributes to the agent's correct invocation.

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 tool with 6 parameters, 3 required, and no output schema, the description provides an unusually complete picture. It describes what the tool returns (carried token volume, cost in USD, comparative cost at 20 turns from the end, and the multiplier), explains the pricing formula, and gives an application scenario. Nothing essential is missing for an agent to call it correctly.

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 already documents each parameter. The description adds value by explaining the cost model in relation to the parameters (e.g., 'a token costs its size times the number of turns left after it arrives'). This contextualizes size_tokens, entry_turn, and total_turns beyond their individual definitions. The description goes beyond simply restating schema details.

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 opens with a specific verb+resource: 'Price a block of context by where it enters a session.' It clearly distinguishes this tool from siblings like assess_workspace or search_architecture, which are unrelated. The stateless context and pricing formula are explained succinctly, leaving no ambiguity about the tool's function.

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 explicitly states a use case: 'Use it to decide whether a large read should move later in a session or into a subagent.' It does not mention alternatives or when not to use it, but given the sibling tools are not competitors, the guidance is adequate. It provides clear context for when this tool adds value.

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