Search Cars.com for vehicle listings. Returns prices, mileage, dealer info.
Args: make: Car make (e.g. 'Toyota') model: Car model (optional, e.g. 'Camry') zip_code: ZIP code for local results (optional) max_results: Max results (default 20)
| Name | Required | Description | Default |
|---|---|---|---|
| make | No | ||
| model | No | ||
| zip_code | No | ||
| max_results | No |
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.
Add one secure layer between your agents and this server.
The two tools are clearly distinct: car_market_analysis provides aggregated market price ranges, while search_cars returns specific vehicle listings. There is minor overlap in the domain (both involve car prices), but their purposes are easy to differentiate.
search_cars follows a verb_noun pattern, while car_market_analysis is a noun phrase, so the naming convention is not fully consistent. However, both use snake_case and are readable, so the inconsistency is minor given the small tool count.
With only 2 tools, the server feels under-scoped for an 'automotive' MCP. The tools cover two core functions, but the count is thin and could benefit from additional capabilities like vehicle details or comparisons.
The server covers market analysis and listing search, which are two key aspects of the automotive domain. However, it lacks common operations like retrieving specific vehicle details, comparing models, or accessing historical trends, leaving notable gaps.