llm_research

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

No annotations are provided, so the description carries the full burden. It mentions routing to Perplexity, indicating web search behavior, but lacks details on output format, rate limits, authentication, or potential side effects. It adds some transparency but is minimal.

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 very concise: one sentence for purpose, one sentence for best uses, and a bullet list of arguments. Every sentence adds value, and the core purpose is front-loaded.

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?

The tool has an output schema, so return values are covered. However, the description does not explain how the research process works (e.g., whether it uses conversation history, how Perplexity integration behaves). Feels somewhat incomplete for a tool that relies on an external service.

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%, so the description must add meaning. It provides brief explanations for each parameter (e.g., 'The research question' for prompt), but these are only slightly more informative than the schema titles. Does not fully compensate for the lack of parameter documentation.

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 performs a search-augmented research query routed to Perplexity for web-grounded answers, and lists specific use cases (fact-checking, current events, sources, market research). This differentiates it from other llm_ tools like llm_query or llm_analyze.

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 description explicitly says 'Best for: fact-checking, current events, finding sources, market research,' providing clear context for when to use the tool. It does not mention when not to use it or name alternatives explicitly, but the guidance is strong enough.

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