validate_subagent

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool does (structural validation of specific fields), what it returns (validation result with errors and warnings), and important behavioral constraints ('No LLM calls — pure structural validation'). However, it doesn't mention error handling, performance characteristics, or other operational 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?

The description is efficiently structured in three sentences that each earn their place: first states the tool's purpose and scope, second specifies what it checks, third describes the return value and important constraint. No wasted words, front-loaded with essential 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?

Given the tool's moderate complexity (validation with specific field requirements), no annotations, and no output schema, the description provides good coverage of what the tool does, what it validates, and its behavioral constraints. However, without an output schema, it could more explicitly describe the structure of the validation result it returns.

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 the single 'response' parameter. The description adds context that this is 'The JSON object returned by a graph-analysis subagent' and mentions the specific fields being validated, but doesn't provide additional syntax or format details beyond what the schema provides. Baseline 3 is appropriate when schema does the heavy lifting.

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 ('validate', 'checks', 'returns') and identifies the resource ('subagent responses from scatter-gather graph traversal'). It distinguishes from siblings by specifying it validates subagent responses rather than performing other operations like asking, consulting, or generating.

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 implies usage context ('for subagent responses from scatter-gather graph traversal') but doesn't explicitly state when to use this tool versus alternatives. It mentions 'No LLM calls — pure structural validation' which provides some guidance on its scope, but doesn't name specific sibling tools or provide explicit when/when-not instructions.

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