Skip to main content
Glama
BACH-AI-Tools

Vehicle Database MCP Server

makes_6

Retrieve vehicle makes available for recall checks by year to identify potential safety issues.

Instructions

Provides a list of makes available for vehicle recalls 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 describes a read operation ('provides a list'), which implies it's non-destructive, but doesn't mention any behavioral traits like rate limits, authentication needs, error handling, or response format. For a tool with no annotations, this leaves significant gaps in understanding how it behaves beyond basic functionality.

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 directly states the tool's function without unnecessary words. It's front-loaded with the core purpose, making it easy to parse. However, it could be slightly more structured by explicitly mentioning the parameter, but overall it's concise and well-formed.

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

Completeness3/5

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

Given the tool has one parameter with full schema coverage and no output schema, the description is minimally adequate. It covers the basic purpose but lacks details on usage context, behavioral traits, and differentiation from siblings. For a simple read tool, it meets the bare minimum, but improvements in guidelines and transparency would enhance completeness.

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 description adds minimal semantic context by implying the 'year' parameter is used to filter makes for vehicle recalls, but the input schema already has 100% coverage with a clear description for the 'year' parameter. Since schema coverage is high, the baseline is 3, and the description doesn't provide additional details like format examples or constraints beyond what's in the schema.

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 vehicle recalls API by year.' It specifies the verb ('provides a list'), resource ('makes'), and context ('for vehicle recalls API by year'). However, it doesn't explicitly differentiate from sibling tools like 'makes', 'makes_2', 'makes_3', etc., which appear to be similar list operations, leaving some ambiguity about why this specific tool exists among them.

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 year' as a parameter, but doesn't specify prerequisites, exclusions, or compare it to other 'makes' tools in the sibling list. This lack of context makes it unclear why an agent would choose this over other similar tools.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/BACH-AI-Tools/bachai-vehicle-database'

If you have feedback or need assistance with the MCP directory API, please join our Discord server