[Automotive Intelligence] 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 readOnlyHint=true (safe read) and openWorldHint=true (data may change). The description adds that it searches Cars.com and returns specific fields, consistent with annotations. No contradictions, but limited additional insight 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?
Extremely concise: one sentence summarizing purpose and return data, followed by a clean Args list. No redundant information, front-loaded with key action.
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?
For a straightforward 4-parameter search tool with no output schema, the description covers purpose, parameters, and return fields. Lacks details on response format or pagination, but adequate for basic usage.
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?
Input schema has 0% description coverage. The description's Args section explains each parameter (make, model, zip_code, max_results) with examples and defaults, effectively compensating for the missing 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?
Description clearly states the tool searches Cars.com for vehicle listings and specifies returned data (prices, mileage, dealer info). The verb 'search' and resource 'Cars.com' are specific, distinguishing it from the sibling 'automotive__car_market_analysis'.
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 explicit guidance on when to use this tool versus alternatives. It does not mention when not to use or provide comparisons with the sibling 'automotive__car_market_analysis' or similar search tools from other domains.
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.
Many tools have overlapping functionality across categories (e.g., multiple search_arxiv, search_google_scholar, real estate tools, DNS/WHOIS checks). An agent would struggle to differentiate between similar tools from different categories, leading to ambiguity.
Tools follow a 'category__verb_noun' pattern mostly, but verbs vary (get, search, screen, check, etc.) and some categories use different orders (e.g., 'get_repo_stats' vs 'search_repos'). The consistency is acceptable but not uniform across the entire set.
With 152 tools, the server is excessively large for a single MCP server. While it aims to be an all-in-one gateway, the sheer number overwhelms the agent and likely exceeds practical limits for coherent selection.
The server covers a wide range of domains (finance, real estate, news, developer tools, etc.) but has notable gaps (e.g., social media APIs, CRM tools). Coverage is broad but not exhaustive, and some niche areas (e.g., global stock exchanges) are over-represented.