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BACH-AI-Tools

Vehicle Database MCP Server

vehicle_maintenance

Retrieve OEM maintenance schedules for vehicles from 2001-2022 by providing year, make, and model to access recommended service intervals and procedures.

Instructions

This API provides information about the OEM vehicle maintenance schedules at mileage intervals Coverage: This API supports VIN from year 2001 to 2022. Support: 17 digit VIN number.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYesExample value: 2019
makeYesExample value: jeep
modelYesExample value: Wrangler Unlimited
Behavior2/5

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

No annotations are provided, so the description carries full burden. It implies a read-only operation by stating it 'provides information,' but doesn't disclose behavioral traits like whether it requires authentication, rate limits, error conditions, or what format the maintenance schedule information returns. The VIN coverage statement adds some context but is inconsistent with the schema.

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

Conciseness2/5

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

The description is poorly structured with run-on sentences and lacks clear organization. It front-loads the purpose but includes contradictory information about VIN that doesn't match the schema. The sentences about coverage and support don't earn their place as they mislead rather than clarify.

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 no annotations and no output schema, the description is incomplete. It fails to explain what the output looks like (e.g., maintenance schedule details), doesn't clarify the parameter mismatch, and offers minimal behavioral context. For a tool with 3 required parameters and no structured output documentation, this leaves significant gaps.

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 each parameter having example values in the schema. The description doesn't add any meaningful parameter semantics beyond what's in the schema—it mentions VIN requirements that don't align with the actual parameters. Baseline is 3 since the schema does the heavy lifting, but the description adds confusion rather than value.

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

Purpose3/5

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

The description states the tool provides 'information about the OEM vehicle maintenance schedules at mileage intervals' which clarifies it's a read operation about maintenance schedules. However, it doesn't specify what kind of information (e.g., schedule details, intervals, recommended services) or distinguish itself from sibling tools like 'vehicle_repair' or 'vehicle_warranty' that might also provide vehicle-related information.

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 mentions coverage for VINs from 2001-2022 and requires a 17-digit VIN, but this contradicts the input schema which requires year, make, and model parameters (not VIN). There's no guidance on when to use this tool versus alternatives like 'decode_by_vin' or 'vehicle_repair', and the VIN mention is confusing given the actual parameters.

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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