US Government Open Data MCP

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

No annotations are provided, so the description carries the full burden. It discloses behavioral traits such as the dataset size (174K+ records) and mentions count fields (e.g., country_code, advisory_committee), which adds useful context. However, it lacks details on rate limits, authentication needs, error handling, or pagination behavior, leaving gaps for a search tool.

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 appropriately sized and front-loaded: it starts with the core purpose, provides examples in a bulleted list, and ends with count fields—all sentences earn their place without redundancy. It efficiently conveys necessary information in a structured manner.

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 tool's complexity (search with two parameters), no annotations, and no output schema, the description is fairly complete. It explains the purpose, provides usage examples, and mentions count fields, covering key aspects. However, it lacks details on output format or error cases, which could be improved for full completeness.

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 100%, so the baseline is 3. The description adds value beyond the schema by providing concrete search examples (e.g., 'advisory_committee:cv', 'device_name:"pacemaker"') and listing count fields, which help clarify how to use the 'search' parameter effectively. This compensates well, though it doesn't detail parameter interactions.

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: 'Search 510(k) premarket clearance decisions (174K+ since 1976). A 510(k) demonstrates a device is substantially equivalent to a legally marketed device.' It specifies the verb ('Search'), resource ('510(k) premarket clearance decisions'), and scope (174K+ records since 1976), distinguishing it from sibling FDA tools like fda_device_classification or fda_device_pma.

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 usage through multiple example searches (e.g., 'advisory_committee:cv', 'device_name:"pacemaker"'), which implicitly guide when to use this tool for querying FDA device clearance data. However, it does not explicitly state when not to use it or name alternatives among sibling tools, though the examples help differentiate its search functionality.

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