deep_research

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

With no annotations provided, the description carries the behavioral burden. It reveals the grounding method (Google Search), that it synthesizes multiple sources, and that it prioritizes thoroughness over speed. It does not detail output structure, citations, or failure behavior, but the core behavioral expectations are clear.

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 sentences with no filler: core behavior, method, and usage context are all front-loaded. Every sentence earns its place.

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 two-parameter tool with no output schema or annotations, it explains what the tool does, how it works, when to use it, and what it returns at a high level. It could add more on expected output details (e.g., citations or format), but nothing essential to selecting and invoking it correctly is missing.

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 100%, so the schema already documents both parameters. The description adds no extra parameter meaning beyond confirming the topic should be specific, which is already in the schema; this matches the baseline of 3 for high 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 uses a specific verb and resource ('conduct deep, multi-angle research on a topic using Google Search grounding') and differentiates itself from web_search and fact_check by emphasizing multi-source synthesis and comprehensive analysis. An agent can tell it is the heavier research tool rather than a quick lookup or verification tool.

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 explicit trigger conditions: complex topics, technical deep-dives, comparisons, and when thoroughness outweighs speed. It does not name sibling tools as alternatives or state 'for quick lookups use web_search', so it stops short of a full when/when-not matrix.

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