gemini-researcher

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

With no annotations provided, the description carries full burden. It mentions 'comprehensive analysis' and 'deep reasoning' which suggests intensive processing, but doesn't disclose performance characteristics, rate limits, authentication needs, or what 'deep reasoning' entails operationally. The example helps but doesn't fully compensate for missing behavioral details.

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?

Perfectly front-loaded with the core purpose in the first sentence, followed by usage guidance and a concrete example. Every sentence earns its place - no redundant information, no wasted words. The structure moves from general to specific effectively.

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 analysis tool with 3 parameters and no annotations or output schema, the description provides good purpose and usage context but lacks details about what the analysis actually produces, how comprehensive it is, performance implications, or error conditions. The example helps but doesn't fully address the complexity gap.

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 all parameters thoroughly. The description's example shows parameter usage in context ('prompt: 'Trace authentication flow...', focus: 'architecture', citationMode: 'paths_only''), which adds some practical semantics but doesn't provide significant additional meaning beyond what the schema already specifies.

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's purpose with specific verbs ('perform comprehensive codebase analysis', 'deep reasoning') and resources ('across multiple files'). It distinguishes from siblings by specifying it's for 'complex architectural questions or multi-file investigation', unlike simpler tools like quick_query or analyze_directory.

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?

Explicitly states when to use this tool ('preferred for complex architectural questions or multi-file investigation') and provides a concrete example that demonstrates appropriate usage context. This gives clear guidance on when to choose deep_research over alternatives like quick_query or analyze_directory.

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