[Automotive Intelligence] 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, so the description doesn't need to reinforce that. It adds that it returns price ranges and dealer inventory, but no further behavioral details like data sources, rate limits, or caching. Acceptable but not rich.
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 concise, front-loading the purpose. The args are listed in a structured way. It could be more scannable with bullet points, but overall efficient.
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 read-only analytics tool with no output schema, the description gives a high-level summary of return values ('price ranges and dealer inventory') but lacks specifics on format or structure. Leaves some ambiguity about what exactly is returned.
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 coverage is 0%, so description bears full burden. It lists all three parameters with examples and clarifies that year is optional with default 0. This adequately adds meaning beyond the raw schema.
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 analyzes car market prices for a specific make/model and returns price ranges and dealer inventory. It distinguishes itself from sibling 'automotive__search_cars' which likely searches for car listings, not 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?
The description implies usage for price analysis of a specific make/model, but does not explicitly contrast with siblings or state when not to use. No alternative tools are mentioned.
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.