newton_embed_timeseries

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

Annotations cover the safety profile (readOnlyHint, openWorldHint), so the bar is lower; the description still adds real behavioral context by specifying the input layout semantics ('channel-first') and the output cardinality (one 768-dim embedding per channel), which the annotations cannot convey. It does not disclose the model default behavior, but that is minor.

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?

Three tight sentences, front-loaded with purpose, then input shape, then the normalize caveat. No filler.

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 present, the return value needn't be re-explained, and the description covers the non-obvious input structure and the normalize tradeoff. The undocumented 'model' parameter is the only gap.

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 0%, so the description must carry the load, and it does for the critical 'channels' parameter ('outer list = channels, inner lists = samples') and gives a decision rule for 'normalize'. Only the 'model' parameter is left unexplained.

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 and resource ('Encode a sensor window'), names the concrete encoder ('Newton Omega'), and its scope is narrow enough to distinguish it from the sibling newton_query without opening the 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?

No guidance on when to choose this tool over newton_query, and no prerequisites described. The only conditional ('Leave normalize=false unless...') is a parameter-level instruction, not a when-to-use rule.

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