newton_query

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

Annotations already declare readOnlyHint and openWorldHint, so safety and open-domain behavior are covered. The description adds genuinely useful behavioral context (it is a reasoning model that can be grounded with events/files and steered via system_prompt), but says nothing about latency, cost, token limits, or failure modes of an LLM call.

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 the core action and then the grounding/steering options. Every sentence contributes, with only minor duplication of the 'model' concept.

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?

An output schema exists so return values need not be explained, and annotations cover the read-only profile. Still, with 7 parameters at 0% coverage, the omission of model and max_new_tokens and of event-array formats leaves an agent guessing on nontrivial 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% across 7 parameters, so the description carries the burden. It explains query, file_ids, json_events, text_events, and system_prompt, but leaves model and max_new_tokens entirely undocumented and gives no format details for the event arrays.

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 (Ask) and resource (Newton, a text-reasoning model) scoped to physical-world data, which is clearly distinct from the sibling newton_embed_timeseries. It stops short of explicitly naming the sibling to differentiate, so it lands just below the top band.

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?

It gives concrete invocation guidance — ground the query with inline text/JSON events or previously uploaded file_ids, and use system_prompt to force structured JSON. However, it never states when to prefer this over newton_embed_timeseries or any prerequisites for the grounding inputs, leaving the choose-a-tool decision to inference.

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