US Government Open Data MCP

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 effectively describes key behavioral traits: it's a search tool (implying read-only operation), notes that booleans are stored as strings, provides example query syntax, and mentions count fields for aggregation. It lacks details on rate limits, authentication needs, or error handling, but covers essential operational context.

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 well-structured and front-loaded with the core purpose, followed by important notes and practical examples. Every sentence adds value: the first states what it does, the second clarifies data format, the examples demonstrate usage, and the final line highlights useful aggregation fields. No wasted words.

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 moderate complexity (search with two parameters), no annotations, and no output schema, the description does a strong job of completeness. It explains the purpose, provides usage examples, clarifies data quirks (boolean strings), and hints at result structure (count fields). It could improve by explicitly describing the output format or error cases, but it's largely sufficient for effective use.

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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds value by providing concrete search examples that illustrate how to use the 'search' parameter with various query types (field:value, exact phrases, exists checks) and mentions count fields, which helps interpret results beyond the basic limit parameter. This goes beyond the schema's generic examples.

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 searches the Global Unique Device Identification Database (GUDID) for detailed device records, specifying the database and types of information returned (description, MRI safety, product codes, sterilization). It distinguishes itself from sibling tools by focusing on medical device identification data, unlike the many economic, health, or legislative tools in the list.

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 this tool through multiple example searches (e.g., by brand, prescription status, MRI safety), which implicitly guides usage. However, it does not explicitly state when not to use it or name alternative tools for similar data, though the sibling list shows no direct FDA device search alternatives.

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