Get information about available recall data sources.
Returns a list of all supported regulatory agencies and their coverage.
This is a free call that does not consume API credits.
Returns:
Dictionary with data sources and their descriptions
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses a key trait: the call is free and does not consume API credits. It also states the return format ('Dictionary with data sources and their descriptions'). While it does not mention side effects or limitations, for a read-only informational endpoint with zero parameters this is adequate and adds value beyond the schema.
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 concise—three short sentences—that front-load the main purpose in the first sentence. The 'Returns:' section is formatted for quick scanning. Every sentence earns its place without redundancy or fluff.
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?
For a zero-parameter tool with a simple output, the description is largely complete: it states what the tool does, what it returns (list of agencies and coverage), and that it is free. It could clarify what 'coverage' means or the exact structure of the returned dictionaries, but for the tool's simplicity this is sufficient.
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 zero parameters, so the description has no parameter meanings to explain. Per the rubric, a tool with zero parameters gets a baseline of 4. The description does not detract from this and appropriately focuses on output rather than inputs.
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's purpose: 'Get information about available recall data sources.' It specifies the scope ('all supported regulatory agencies and their coverage'), which distinguishes it from the sibling tool 'search_recalls' that searches for recall records. The verb 'get' and resource 'data sources' are specific.
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 provides clear context for when to use the tool: when you need a list of data sources or regulatory agencies. It also notes 'This is a free call that does not consume API credits,' implying it can be used without cost concern. However, it does not explicitly mention when not to use it or compare it to alternatives, so it stops short of a 5.
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.
get_data_sources provides metadata about available regulatory agencies, while search_recalls performs the actual product recall search. These are clearly distinct purposes with no functional overlap, making tool selection unambiguous.
Both tools follow a consistent verb_noun pattern with snake_case (get_data_sources, search_recalls). The naming is predictable and matches the server's search-oriented purpose.
With only two tools, the server feels minimal. However, for a specialized search service, a metadata tool and a search tool may be sufficient. Still, the count is on the thin side, potentially limiting discoverability of other useful capabilities.
The core workflow of discovering available data sources and searching for recalls is covered. A minor gap is the lack of a dedicated tool to retrieve detailed information for a specific recall ID, but the search results include sufficient product details, so agents can work around this.