azure_ptu_sizing

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the calculation method ('official rounding rules'), mentions cost estimation as optional, and specifies supported deployment types. However, it doesn't address important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or what the output format looks like (though there's no output schema).

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 efficiently structured in three sentences: first states the core purpose, second explains the calculation method and optional features, third specifies deployment type support. Every sentence adds value with zero wasted words, and the most important information (what the tool does) comes first.

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 9 parameters, no annotations, and no output schema, the description provides adequate but incomplete context. It covers the purpose, calculation method, and optional features well, but doesn't address output format, error conditions, or authentication requirements. Given the complexity and lack of structured metadata, the description should do more to compensate for these gaps.

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 all 9 parameters thoroughly. The description adds some context about workload shape parameters (RPM, tokens, caching) and cost estimation, but doesn't provide additional semantic meaning beyond what's in the parameter descriptions. The baseline of 3 is appropriate when the schema does the heavy lifting.

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 specific action ('Estimate required Provisioned Throughput Units'), identifies the target resource ('Azure OpenAI / AI Foundry model deployments'), and distinguishes from siblings by focusing on PTU sizing rather than cost analysis, SKU discovery, or other Azure operations. It specifies the calculation method ('based on workload shape with official rounding rules') and optional cost estimation.

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 about when to use this tool: for estimating PTU requirements for Azure OpenAI/AI Foundry deployments with specific workload parameters. It mentions optional cost estimation via Azure Retail Prices API, which helps differentiate from pure cost tools. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools.

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