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 of behavioral disclosure. It describes what data is returned but lacks critical behavioral details such as whether this is a read-only operation (implied by 'Get'), potential rate limits, authentication requirements, error handling, or data freshness. For a tool with no annotations, this leaves significant gaps in understanding its operational behavior.

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 efficiently structured in two sentences: the first states the core purpose, and the second elaborates on the specific data points included. Every sentence adds value without redundancy, making it front-loaded and easy to parse for an AI agent.

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 (3 required parameters, no output schema, no annotations), the description is partially complete. It clearly outlines the returned data but lacks details on behavioral aspects (e.g., error cases, data sources) and output structure. Without an output schema, the description should ideally hint at the response format, but it does not, leaving some contextual gaps.

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%, with clear descriptions for 'make', 'model', and 'model_year' parameters. The description does not add any additional semantic context beyond what the schema provides (e.g., it doesn't clarify parameter formats, dependencies, or examples). Given the high schema coverage, a baseline score of 3 is appropriate as the schema adequately documents the parameters.

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 specific verb ('Get') and resource ('NHTSA 5-star safety ratings for a vehicle'), and distinguishes itself from sibling tools like 'nhtsa_complaints' or 'nhtsa_recalls' by focusing on comprehensive safety ratings that include crash test results, rollover risk, safety technology, and complaint/recall/investigation counts. It goes beyond a simple tautology by detailing what information is retrieved.

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 no guidance on when to use this tool versus alternatives. While it implicitly suggests use for vehicle safety ratings, it does not mention when to choose this over sibling tools like 'nhtsa_complaints' or 'nhtsa_recalls', nor does it specify any prerequisites, exclusions, or contextual constraints for its application.

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