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 describes the tool's function (decoding VIN to specifications) but lacks behavioral details such as error handling (e.g., invalid VINs), data source (NHTSA database), rate limits, authentication needs, or response format. This is a significant gap for a tool with no annotation coverage.

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 a single, efficient sentence that front-loads the core purpose ('Decode a VIN to get vehicle specifications') and follows with a specific list of outputs. Every word adds value, with no redundant or vague phrasing, making it highly concise and well-structured.

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 low complexity (single parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and outputs well, but lacks behavioral context (e.g., error handling, data freshness) and does not compensate for the absence of an output schema or annotations, leaving gaps in understanding the tool's full operation.

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 for the single parameter 'vin', with clear constraints (string, 11-17 characters). The description adds no additional parameter semantics beyond what the schema provides, such as VIN validation rules or examples. With high schema coverage, the baseline score of 3 is appropriate.

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: 'Decode a Vehicle Identification Number (VIN) to get vehicle specifications' followed by a specific list of outputs (make, model, year, engine, body class, drive type, fuel type, manufacturer, plant location). It uses a precise verb ('Decode') and resource ('VIN'), and distinguishes itself from sibling tools (which are unrelated data tools from other agencies like BEA, BLS, CDC, etc.).

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 implies usage context by specifying what the tool returns (vehicle specifications from a VIN), but does not explicitly state when to use it versus alternatives. There are no direct sibling tools for VIN decoding in the list, so no explicit comparison is needed, but it lacks guidance on prerequisites or limitations (e.g., valid VIN formats beyond length).

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