automotive-mcp-server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating a safe read operation. The description adds that it 'Returns price ranges and dealer inventory,' which gives some insight into the output. However, it does not explain the implications of openWorldHint (e.g., results may vary) or other behavioral traits, so the added value is modest.

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 two sentences plus a compact argument list, with no filler. The core purpose appears in the first sentence, making it front-loaded 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?

The tool has a simple interface with 3 parameters and no output schema. The description specifies the output type (price ranges, dealer inventory) but not its detailed structure, which is a minor gap given the absent output schema. Overall, the essential invocation context is present, so it is nearly complete.

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 0%, so the description must carry parameter semantics. It does this well: make and model get examples ('Honda', 'Civic'), and year is documented as optional with '0 for all years.' This adds meaning beyond the raw schema and fully compensates for the lack of schema descriptions.

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 states 'Analyze car market prices for a specific make/model' and specifies return values ('price ranges and dealer inventory'), making it clear what the tool does. This is a specific verb+resource statement that conceptually distinguishes it from a general car search tool.

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 when the agent needs market pricing analysis for a specific make/model, but it does not explicitly contrast with the sibling tool 'search_cars' or provide when-not guidance. The context is clear but lacks exclusion criteria or alternative suggestions.

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

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare the tool read-only and open-world. The description adds that it searches Cars.com and returns specific fields (prices, mileage, dealer info), which is useful context. However, it does not disclose rate limits, pagination, or any edge-case behavior, so it adds only moderate value beyond annotations.

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 one-sentence overview followed by a compact Args list. Every line carries necessary information (examples, optionality, defaults) without redundancy. The structure front-loads the main behavior and keeps parameter semantics organized and scannable.

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?

The description covers core search behavior and parameter meanings, but the return structure is only vaguely specified ('prices, mileage, dealer info') with no output schema to supplement. There is no mention of sorting, filtering, or result limits beyond the default. For a simple search tool this is acceptable but leaves room for clarification.

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 has 0% description coverage, so the Args block fully compensates by explaining every parameter: make (with example), model (optional, with example), zip_code (for local results), and max_results (with default). This is clear and directly addresses the schema gap, though it lacks formatting details like ZIP code format.

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 states a specific verb and resource ('Search Cars.com for vehicle listings') and mentions the returns (prices, mileage, dealer info). It does not explicitly differentiate from the sibling car_market_analysis, but the tool name and search vs analysis function make the distinction reasonably clear.

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?

No guidance is provided on when to use this tool versus car_market_analysis, nor any mention of preferred contexts, prerequisites, or exclusions. The agent is left to infer usage from the tool name and parameters.

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