deep_research

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

With no annotations, the description discloses key behavioral traits: engine auto-selection (CN vs international), deduplication and ranking, fetching top pages, and producing a cited markdown report. This gives good insight into the internal pipeline, though lacks details on failure modes or operational limits.

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 two sentences, front-loaded with the core action, and every word adds value. No wasted information.

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?

For a complex tool with five parameters and no output schema, the description adequately explains the workflow and final output (cited markdown report). It omits some operational details but is sufficiently complete for an agent to select and invoke it correctly.

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 only 40%, and the description helps clarify 'engines' (auto-selection) and 'fetch_top_k' (fetch top pages), but fetch_chars and num_per_engine remain unexplained. The description adds some meaning but does not fully compensate for the low schema coverage.

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 it performs multi-engine deep research by fanning out across engines, deduplicating and ranking results, fetching top pages, and synthesizing a cited markdown report. This specific verb+resource description effectively distinguishes it from sibling tools web_search and fetch_url.

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 ends with 'Use for thorough, multi-source research,' providing clear context for when to select this tool over simpler siblings. However, it does not explicitly state when not to use it or name alternatives.

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