recommend_material

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

No annotations provided, so the description bears full burden. It mentions using 'printer DNA + historical data' and translating natural language intent, but does not explicitly state it is a read-only query (no side effects) or what happens on failure. Still, it adequately conveys the tool's behavior and inputs.

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 well-organized: a concise introductory sentence, a bulleted explanation of the underlying mechanism, a clear 'which tool to use' section, and an Args list. Every part adds value, and there is no unnecessary 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?

Despite no output schema, the description covers purpose, parameters, and usage context well. It lacks details on the output format (e.g., what fields are returned, whether it's a single recommendation or list). This minor gap prevents a perfect score.

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 0%, but the description lists all parameters (intent, has_enclosure, has_heated_bed, budget_usd) with clear explanations and examples for intent. This fully compensates for the lack of schema descriptions and adds meaning beyond the basic schema types.

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 recommends material based on intent and printer capabilities, with specific examples (e.g., 'make it strong'). It distinguishes itself from siblings by explicitly naming alternative tools and their use cases: recommend_design_material and suggest_material_for_order.

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 dedicates a section 'Which material tool to use:' with clear when-to-use (quick intent-based pick for your own printer) and when-not-to-use, pointing to sibling tools for different scenarios. This provides excellent guidance.

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