autonomous_research

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

No annotations exist, so the description must carry the burden. It mentions API key requirements, autonomous execution, and expected duration, but omits details on side effects, error handling, or the loop's internal behavior.

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 concise, with the first sentence immediately conveying the core action and steps. The second paragraph adds essential context on requirements and timing without unnecessary verbosity.

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?

Despite having an output schema, the description lacks details on step-by-step behavior, error scenarios, and parameter explanations. Given the tool's complexity (autonomous multi-loop research) and no annotations, more context is needed for full understanding.

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%, and the description provides no additional meaning for any of the 4 parameters (model, topic, provider, max_rounds). The schema itself has only titles and defaults, leaving agents uninformed about parameter semantics.

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 clearly states the tool runs the full research loop autonomously, listing the steps (screen -> draft -> fact-check -> revise) and distinguishing it from sibling tools like screen_topic and draft_outline.

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 specifies the requirement for an LLM API key, warns about using only when the autonomous model is desired, and notes the time expectation. However, it doesn't explicitly list when to avoid using it or provide direct comparisons to siblings.

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