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

With no annotations, the description carries the full burden of behavioral disclosure. It does state what the tool computes (cluster size, monthly cost, utilization), implying it is a read-only estimation. Yet it does not disclose assumptions, limitations, or whether it requires any external data or actions. This is adequate but not rich.

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 purpose and packs the key inputs/outputs into a concise summary. No filler or redundant details.

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?

The tool has 6 required parameters, no output schema, no annotations, and no parameter descriptions in the schema. The description gives only a high-level overview, omitting important parameters like gpuHourlyUsd, aggregateTokensPerSecPerGpu, utilizationHeadroomPct, and how they affect results. An AI agent would likely need more detail to correctly configure all inputs.

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. It explains the relationship between concurrent users/target tokens and the cluster size/cost, but it does not explicitly describe all parameters (e.g., aggregateTokensPerSecPerGpu, utilizationHeadroomPct). Though param names are mostly self-explanatory, some technical terms like aggregateTokensPerSecPerGpu could use clarification.

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 purpose with a specific verb ('Estimate') and resource ('how many GPUs your LLM needs'), and it distinguishes itself from sibling calculators by focusing on throughput-based GPU sizing, cluster cost, and utilization. It gives a concrete sense of inputs (concurrent users, tokens/sec) and outputs (cluster size, monthly cost, utilization).

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 provides clear context on when to use this tool: when you need to estimate GPU cluster size and cost for an LLM workload based on concurrency and throughput. However, it does not explicitly mention alternatives or when not to use it, but the purpose is specific enough to guide selection.

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