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 must fully disclose behavioral traits. It describes the output content (e.g., 'Shows crash/fire/injury/death counts, affected components, and complaint summaries') but lacks critical details such as whether this is a read-only operation, if there are rate limits, authentication requirements, or how results are formatted (e.g., pagination). For a search tool with no annotation coverage, this is a significant gap in transparency.

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 and well-structured in a single sentence, front-loaded with the core purpose ('Search NHTSA consumer complaints about vehicles') followed by key output details. Every part adds value without redundancy, making it efficient and easy to parse.

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 (a search function with 3 required parameters), lack of annotations, and no output schema, the description is moderately complete. It covers the purpose and output content but misses behavioral aspects like safety profile, rate limits, and result formatting. For a search tool without structured output information, it should provide more context to be fully helpful, but it meets a basic adequacy level.

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 100% description coverage, with clear parameter descriptions (e.g., 'Vehicle make', 'Vehicle model', 'Model year'). The tool description does not add any additional semantic context beyond what the schema provides, such as example values or constraints. Since schema coverage is high, the baseline score of 3 is appropriate, as the schema adequately documents the parameters without extra help from the description.

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 NHTSA consumer complaints about vehicles.' It specifies the verb ('Search'), resource ('NHTSA consumer complaints'), and scope ('about vehicles'), making it easy to understand. However, it does not explicitly differentiate from sibling tools like 'nhtsa_recalls' or 'nhtsa_safety_ratings', which are related but distinct NHTSA tools, so it falls short of a perfect score.

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. It mentions what data is shown (e.g., crash/fire/injury/death counts) but does not specify prerequisites, exclusions, or compare it to other NHTSA tools like 'nhtsa_recalls' for recall data. This lack of contextual usage information limits its effectiveness for an AI agent.

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