uniform_quantization

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

No annotations are provided, so the description carries the transparency burden. It does disclose that the operation is a simulation and lists the numeric outputs, but it does not mention rounding behavior, boundary handling, or out-of-range behavior. Basic behavior is clear, but edge-case behavior is not.

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, front-loaded sentence with no filler. It immediately states what the tool does and what it returns, making the core information easy to scan.

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 no annotations, four required parameters, and zero schema description coverage, the description leaves important semantic gaps. The output schema helps with return values, but the description does not provide enough guidance for an agent to confidently construct valid and correct arguments.

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 missing parameter documentation. It adds meaning to 'value' by calling it a continuous value, but it never explains bits, v_min, or v_max roles. An agent would still need to infer parameter semantics from domain knowledge.

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 names a specific operation ('simulates uniform quantization') and enumerates the outputs: quantized value, quantization error, step size, and theoretical maximum SQNR. This clearly distinguishes it from the algebraic and transform-oriented sibling tools.

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?

No explicit when-to-use or when-not-to-use guidance is given, and no alternative quantization tool is named. The intended use is only implied by the phrase 'simulates uniform quantization,' so the agent must infer the usage context from the tool name and description.

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