Check the interaction between two drugs (by brand or generic name). Returns severity (contraindicated / major / moderate / minor / unknown), mechanism (when known), and source citations. Always include the response disclaimer in your final answer.
| Name | Required | Description | Default |
|---|---|---|---|
| drug_a | Yes | ||
| drug_b | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that the tool returns severity, mechanism, and citations, and that a disclaimer must be included. However, it does not mention whether the operation is read-only, destructive, or any authentication or rate limit requirements.
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 consists of two sentences, each serving a distinct purpose: one for tool functionality and one for usage instruction. No extra words, front-loaded with key 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 simple two-parameter input and no output schema, the description adequately explains the return value (severity, mechanism, citations) and the disclaimer requirement. It is complete enough for an agent to use the tool effectively.
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?
The schema has only string parameters drug_a and drug_b with 0% description coverage. The description adds meaning by stating 'by brand or generic name' and 'two drugs', clarifying that these are drug name inputs. This compensates for the bare schema.
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 'check', the resource 'interaction between two drugs', and specifies output details (severity, mechanism, source citations). It distinguishes from siblings like check_interactions_multi and normalize_drug_name by focusing on exactly two drugs.
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 use for two drugs but does not explicitly state when to prefer this over check_interactions_multi (for multiple drugs) or normalize_drug_name. The instruction to include a disclaimer is present but provides no guidance on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool has a distinct purpose: single pair, multi-pair, and name normalization. No overlap or ambiguity.
All tools follow a consistent verb_noun snake_case pattern (check_interaction, check_interactions_multi, normalize_drug_name).
Three tools are well-scoped for a drug interaction checker: one for single pairs, one for batch checks, and one for name resolution.
Covers the core workflow: name lookup, single interaction, and batch interaction. Missing a tool to retrieve all interactions for a drug, but the multi tool can handle it with multiple calls.