subagent_conditional

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

The description outlines the basic behavior: calling AI to evaluate a condition and branching accordingly. It lacks details on error handling, what happens if the AI response is not 'true'/'false', or any side effects. With no annotations, the description carries the burden but provides only minimal behavioral disclosure.

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, consisting of only two sentences plus a short usage note. No extraneous information. Every sentence serves a purpose.

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 complexity of a conditional branching tool, the description is somewhat complete but misses details like error scenarios, what happens if the AI fails to respond, or the structure of the output. An output schema exists but is not described, so the description could be more helpful.

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 baseline is 3. The tool description does not significantly add meaning beyond what the parameter descriptions already provide (e.g., it repeats the JSON task format). The description adds no new semantic context.

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: 'Execute conditional branching based on AI decision' and explains the process of evaluating a condition and executing one of two tasks. The name 'subagent_conditional' and the explanation distinguish it from sibling tools like subagent_call and subagent_parallel.

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 mentions 'Useful for dynamic workflow control,' which gives general context. However, it does not explicitly state when to use this tool over alternatives, nor does it provide when-not-to-use guidance. Usage is implied but not explicit.

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