Suggest Components for Use Case

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

No annotations are provided, so the description must shoulder the burden of behavioral disclosure. It only mentions 'AI-powered' and 'suggestions', which hints at non-determinism and output type but provides no details on read-only behavior, return format, or edge cases like no matches. This is a significant gap for a tool with no annotation support.

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 a single, front-loaded sentence with no fluff. It states the core purpose immediately. While minimal, it earns its place in conveying the tool's function.

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?

With no output schema and no annotations, the description should explain what the tool returns (e.g., a list of components) and any constraints. It only states the purpose, leaving the agent to guess the output structure and behavior. This is inadequate for safe invocation.

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?

Both parameters are fully described in the schema (100% coverage), so the description is not required to add more. It aligns with the useCase parameter but adds no detail beyond schema. Baseline 3 applies.

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 verb 'Get', the resource 'component suggestions', and the input 'use case description'. This distinguishes it from siblings like list_material_components or get_component_code, which have different purposes.

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 context is clear: use this tool when you have a use case description and need component suggestions. It does not explicitly mention alternatives, but the input condition is well-defined, giving the agent enough to decide. However, it lacks explicit 'when not to use' guidance.

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