list_drug_recalls
FDA drug recalls and enforcement reports. Filters: classification (Class I, II, III), company_id, limit.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| company_id | No | ||
| classification | No |
FDA drug recalls and enforcement reports. Filters: classification (Class I, II, III), company_id, limit.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| company_id | No | ||
| classification | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavior. It implies a read-only listing and names filters, but does not disclose pagination behavior, default limits, output shape, data freshness, or whether some filters interact in non-obvious ways.
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 concise sentences with the main action front-loaded and the filter list compact. Every sentence earns its place with no unnecessary filler.
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?
This is a simple list tool with three optional filters, and the description provides enough to attempt a basic call. However, without annotations or an output schema, it lacks detail on return values, defaults, and any filtering constraints, leaving some uncertainty for an agent.
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%, so the description must compensate. It names all three parameters and adds enum values for classification, which is helpful. However, it does not clarify the expected format of company_id or the precise meaning and behavior of limit.
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 states a specific verb ('list') and a clear resource ('FDA drug recalls and enforcement reports'), which distinguishes it from sibling tools that focus on shortages or contracts. It is not vague, though it could more fully specify the scope of 'enforcement reports'.
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 lists filters but provides no guidance on when to use this tool versus the sibling alternatives. No exclusions, use-case context, or conditions are given, so an agent has to infer applicability.
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.