Get a FineRx drug's details: coverage stats and its strength/form variants.
``slug`` comes from search_drugs. Returns overall stats (product/package/
chain/vendor counts, fromPrice, latest observation date) and the list of
variants (strength x form) each with a fromPrice.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds value by detailing the return structure (overall stats and variants), going beyond the annotations. No contradictions.
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 two sentences, front-loaded with the purpose and immediately useful details. Every sentence adds value with no waste.
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 input (one param), rich annotations, and existence of an output schema, the description covers the main purpose and output well. Could mention error handling or pagination, but not necessary for this scope.
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 input schema has 0% description coverage for the slug parameter, but the description explains that slug comes from search_drugs, providing important semantic context about its origin. This compensates for the schema gap.
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) and resource (FineRx drug's details), listing specific outputs (coverage stats, strength/form variants). It distinguishes from siblings by mentioning that the slug comes from search_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 explicitly mentions that the slug parameter comes from search_drugs, guiding the user to use this tool after a search. This provides clear context and links to a sibling tool, though it does not explicitly state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool serves a distinct function: searching drugs, getting drug details, comparing prices, finding pharmacies, and retrieving dataset metadata. There is no overlap in purpose.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., search_drugs, compare_prices, get_drug), making the set predictable and easy to navigate.
With 5 tools, the server is well-scoped for a drug pricing API. Each tool addresses a specific step in the typical workflow (search, details, price comparison, location, metadata), and the count is neither too small nor excessive.
The toolset covers the core workflow: search drugs, retrieve drug details, compare prices, find pharmacies, and obtain dataset info. A minor gap is that the mechanism to obtain an NDC from a drug is implied but not explicitly detailed, though the flow likely works.