deep_research

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

With no annotations, the description must disclose behavioral traits. It explains the tool performs deep reasoning across multiple files but does not mention side effects, auth needs, or performance implications. The example gives some insight into usage but lacks comprehensive behavioral context.

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 extremely concise, consisting of two sentences and an example. The purpose is front-loaded, and every element serves a clear function with no wasted words.

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?

Given the tool's complexity and the fact that there is no output schema, the description covers the main aspects: what it does, when to use it, and how to use it via the example. It could be improved by mentioning any limitations or typical use cases, but it is largely complete.

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 coverage is 100% with descriptions for all parameters. The description adds value by providing a concrete example that demonstrates how to use the parameters, especially the prompt and focus/citationMode options, which goes beyond the schema definitions.

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 comprehensive codebase analysis with deep reasoning, and it distinguishes itself from siblings by specifying it is preferred for complex architectural questions or multi-file investigation. The example further solidifies its purpose.

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 states when to use this tool ('complex architectural questions or multi-file investigation'), providing clear context. However, it does not mention when not to use it or suggest alternatives, so it lacks explicit exclusions.

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