Analyze car market prices for a specific make/model. Returns price ranges and dealer inventory.
Args: make: Car make (e.g. 'Honda') model: Car model (e.g. 'Civic') year: Model year (optional, 0 for all years)
| Name | Required | Description | Default |
|---|---|---|---|
| make | Yes | ||
| year | No | ||
| model | Yes |
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.
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.