Vehicle Database MCP Server

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 states the tool provides a list, implying a read-only operation, but doesn't cover critical aspects like authentication requirements, rate limits, error handling, or response format. For a tool with zero annotation coverage, this minimal description leaves key behavioral traits undisclosed.

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 directly states the tool's purpose without any fluff or redundancy. It's appropriately sized for a simple tool and front-loaded with the core functionality, making it easy for an agent to parse quickly. Every word earns its place.

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 simplicity (0 parameters, no output schema, no annotations), the description is minimal but adequate for basic understanding. However, it lacks context about the API's scope (e.g., global vs. regional years, data freshness) and doesn't address sibling tool differentiation, which is crucial in this crowded namespace. For a tool with no structured support, more completeness would help the agent use it effectively.

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 tool has 0 parameters, and the schema description coverage is 100% (as there are no parameters to describe). In such cases, the baseline score is 4, as there's no need for the description to compensate for parameter documentation. The description doesn't add parameter semantics, but that's unnecessary here, so it meets expectations.

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: 'Provides a list of years available for electric vehicle Specifications API.' It specifies the verb ('provides'), resource ('list of years'), and domain context ('electric vehicle Specifications API'), which is clear and specific. However, it doesn't explicitly differentiate from sibling tools like 'year', 'year_2', 'years', 'years_2', etc., which likely serve similar or overlapping functions, preventing 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. With multiple sibling tools related to years (e.g., 'year', 'years', 'years_2', 'years_3', 'years_4', 'years_5', 'years_market_value'), there is no indication of context, prerequisites, or distinctions, leaving the agent to guess based on names alone. This lack of usage guidance is a significant gap.

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