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BACH-AI-Tools

Vehicle Database MCP Server

vehicle_repair

Get repair cost estimates for vehicle parts with labor pricing. Input vehicle year, make, and model to retrieve detailed repair information.

Instructions

This API provides detailed information about the vehicle parts with price and labor cost. Coverage: This API supports manufacturers from year 1992 to 2022.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYesExample value:
makeYesExample value:
modelYesExample value:
Behavior2/5

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

With no annotations provided, the description carries full burden but only states it 'provides detailed information', implying a read-only operation without specifying data format, rate limits, authentication needs, or error handling. It lacks behavioral details like whether it returns structured data, pagination, or any side effects.

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 concise with two sentences that directly state the tool's function and coverage range. It's front-loaded with the main purpose, though the second sentence could be integrated more smoothly for better flow.

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 for a tool with 3 parameters. It doesn't explain return values, error cases, or how the output relates to parts information, leaving significant gaps in understanding the tool's behavior and results.

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%, so the schema documents the three required parameters (year, make, model). The description adds no parameter-specific information beyond implying they relate to vehicle identification, maintaining the baseline score of 3 as the schema handles the heavy lifting.

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 detailed information about vehicle parts with price and labor cost', which gives a general purpose but lacks specificity about what kind of information or format. It doesn't clearly distinguish from sibling tools like 'vehicle_maintenance' or 'vehicle_recall', making it somewhat vague.

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 manufacturers from 1992 to 2022, which provides some context, but offers no explicit guidance on when to use this tool versus alternatives like 'decode', 'by_vin', or other vehicle-related tools. There are no usage exclusions or comparisons to siblings.

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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