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BACH-AI-Tools

Vehicle Database MCP Server

models_4

Retrieve vehicle models for repairs by specifying year and make. This tool helps mechanics and service centers identify compatible parts and procedures.

Instructions

Provides a list of models available for vehicle repairs API by a given year and make.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYesExample value:
makeYesExample value:
dataYesExample value: repair
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 the tool provides a list, implying a read-only operation, but doesn't mention any behavioral traits such as rate limits, authentication requirements, error handling, or response format. For a tool with zero annotation coverage, this is a significant gap in transparency.

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

Conciseness4/5

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

The description is a single, efficient sentence that front-loads the core purpose. It avoids redundancy and wastes no words, though it could be slightly more structured by separating usage context from the action.

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 (3 required parameters, no output schema, and no annotations), the description is incomplete. It doesn't cover behavioral aspects like response format, error cases, or usage constraints, and it lacks differentiation from sibling tools. For a tool in a crowded namespace with no structured support, this leaves significant gaps for an agent.

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?

Schema description coverage is 100%, with all parameters documented in the schema. The description adds minimal value beyond the schema by implying that 'year' and 'make' are used to filter models, and 'data' relates to 'repair', but it doesn't explain parameter interactions or provide additional context. This meets the baseline score of 3 when schema coverage is high.

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 models available for vehicle repairs API by a given year and make.' It specifies the verb ('Provides a list'), resource ('models'), and context ('for vehicle repairs API'), distinguishing it from generic model-listing tools. However, it doesn't explicitly differentiate from sibling tools like 'models', 'models_2', etc., which likely serve similar purposes.

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. It mentions 'by a given year and make' but doesn't specify prerequisites, exclusions, or compare it to other model-related tools in the sibling list (e.g., 'models', 'models_2', 'models_5'). This leaves the agent without context for tool selection.

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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