deep_research_research_query

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

The description adds useful context about routing through the platform's domain-agent dispatcher under JWT, tenant, and company scope. However, it does not disclose any side effects or destructive potential, even though the annotations mark the tool as destructive and non-idempotent. The annotations cover the safety profile, and the description adds modest auth/scoping context, so this is not a contradiction.

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 brief, structured, and front-loads the action, routing behavior, and scope before listing arguments. It avoids unnecessary filler and is 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 an output schema present, the description does not need to explain return values, and the parameter list is short. However, the semantic gap remains: an agent cannot tell from this description what research_query does, what inputs it expects beyond free text and JSON, or what side effects it may have.

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?

Since schema description coverage is 0%, the description carries the burden of explaining parameters. It does clarify that `message` is a free-text objective and `inputs` is an optional JSON string of structured inputs, which adds meaning beyond the bare schema titles. Still, it lacks examples, accepted input shapes, or how `message` and `inputs` interact.

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 says it runs the deep_research domain agent action `research_query`, which is close to restating the tool name and does not explain what research_query actually accomplishes. The 'message: Free-text objective' line hints at the purpose, but the core behavior is left vague.

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?

There is no guidance about when to use this tool versus alternatives like deep_research_chat, deep_research_synthesize, or other domain-agent dispatch tools. It neither states when this is the right choice nor explains when a sibling would be more appropriate.

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