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BACH-AI-Tools

Vehicle Database MCP Server

makes_5

Retrieve vehicle makes available for a specific year to support YMM specifications queries in the Vehicle Database MCP Server.

Instructions

Provides a list of makes available for YMM specifications API by year.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYesExample value:
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it 'provides a list,' implying a read-only operation, but doesn't cover aspects like rate limits, authentication needs, error handling, or output format. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence that efficiently conveys the core functionality without unnecessary words. It's front-loaded with the main action and resource, making it easy to understand at a glance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (one parameter, no output schema, no annotations), the description is minimal. It states what the tool does but lacks details on usage guidelines, behavioral traits, or how it fits among siblings. For a tool in a crowded namespace with no output schema, more context would be helpful to ensure proper agent selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with one parameter 'year' documented as 'Example value: '. The description adds context by specifying it's for 'YMM specifications API by year,' which ties the parameter to the tool's purpose. However, it doesn't provide additional details like format or constraints beyond what the schema implies, so it meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Provides a list of makes available for YMM specifications API by year.' It specifies the verb ('provides'), resource ('list of makes'), and context ('YMM specifications API by year'). However, it doesn't explicitly differentiate from sibling tools like 'make', 'makes', 'makes_2', etc., which appear to be related, so it doesn't reach the highest clarity level.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools (e.g., 'make', 'makes', 'makes_2' through 'makes_7', 'makes_market_value'), there's no indication of how this tool differs or when it's appropriate. The context 'by year' is implied but not compared to other tools.

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

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