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

Annotations already declare readOnlyHint=true, idempotentHint=true, and non-destructive behavior, so the safety profile is covered. The description adds useful context: the concrete compression ratios (4x/8x) and the fact that it 'simulates' rather than mutates storage. Return values are partly duplicated from the output schema but the ratio/trade-off framing adds value.

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?

Four tight sentences, front-loaded with what the tool does, then ratios, return values, and motivation. Every sentence contributes distinct information with no wasted prose.

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 an output schema covering return values, 100% parameter coverage, and annotations carrying the safety profile, the description provides everything an agent needs to decide to call it and how. Nothing material is missing.

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 coverage is 100% and both parameters are fully documented in the schema, so the description is expected to do little extra. It mentions int8/int4 which maps to the 'bits' parameter but adds no new syntax or format guidance beyond the schema. Baseline 3 is appropriate.

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?

States a specific verb ('simulate quantization'), the exact resource ('float32 embedding vectors'), and the scope (int8 or int4). An agent can distinguish it from siblings like vector_stats or normalize_vector without opening any schema.

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 final sentence gives a concrete use case ('understanding vector DB compression trade-offs'), providing clear positive context for invocation. It doesn't name alternatives like vector_stats or explicitly state when NOT to use it, keeping it just below a 5.

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