classify_system

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

Annotations already establish read-only, closed-world, idempotent, non-destructive behavior. The description adds useful behavioral detail beyond annotations: unknown inputs trigger questions and multiple applicable patterns should be combined. It still does not describe the exact response shape, but it adds meaningful 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?

Three short sentences, front-loaded with the entry-point instruction, then purpose, then unknown-case behavior. Every sentence earns its place with no redundant text.

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 seven-parameter classification tool with no output schema, the description covers the entry point, what is classified, and the special handling of unknown inputs. It leaves the exact output structure implicit, but the pattern/question language gives enough for an agent to invoke it.

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?

All seven parameters are optional yes/no/unknown enums with 100% schema description coverage, so the schema already carries parameter semantics. The description only says features are explicit and applicable patterns are combined; it adds no parameter-specific detail. Baseline 3 is appropriate.

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 leads with a specific action ('Start an AI security audit here') and states the resource/output ('Identify ML, GenAI and agentic deployment patterns from explicit system features'). The phrase 'Start ... here' positions it as the entry point relative to audit siblings such as build_audit_plan and investigate_risk.

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 a clear usage context: begin an AI security audit with this tool. It does not explicitly list when to use each sibling or when not to use this tool, so it falls short of 5, but the entry-point guidance is stronger than implied usage.

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