check_interaction

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

No annotations are provided, so the description fully bears the burden. It discloses the return fields (interaction_type, severity, mechanism, etc.), explains that it queries both directions, and states it is descriptive with sources, not medical advice. This gives an agent a clear understanding of behavior.

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 highly concise and well-structured. It starts with the main purpose, then the two usage modes, followed by return information, and ends with important caveats. Every sentence adds value without redundancy.

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 having no output schema, the description lists all relevant return fields and covers input, output, limitations, and authoritative disclaimers. The tool is simple, and the description provides enough detail for an agent to use it correctly and interpret results.

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 coverage is 100%, and the description adds significant meaning beyond the schema. It explains the role of each parameter, provides examples (e.g., 'iron', 'calcium' + 'iron'), and clarifies the optional nature of ingredient_b with the effect of omitting it. This helps an agent craft correct invocations.

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 it checks Clarity's curated ingredient-to-ingredient interaction database. It specifies the two usage patterns (one ingredient to list all, two to check a pair) and distinguishes itself from sibling tools like check_ingredient or check_stack, which focus on other aspects.

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 explains when to use the tool (to check ingredient interactions) and how to use it (with one or two ingredients). It also provides an important caveat that absence of a result is not proof of safety. It lacks explicit when-not-to-use or direct sibling comparisons, but the examples and context suffice.

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