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BACH-AI-Tools

Vehicle Database MCP Server

makes_market_value

Retrieve vehicle makes available for market value assessment by year to identify eligible models for valuation analysis.

Instructions

Provides a list of makes available for market value API by year.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYesExample value: 1999
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 mentions it 'provides a list' but doesn't disclose behavioral traits like pagination, rate limits, authentication needs, or error handling. For a tool with no 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.

Conciseness5/5

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

The description is a single, efficient sentence that front-loads the purpose without unnecessary words. Every part earns its place by specifying the action, resource, and context concisely.

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 doesn't explain what the output looks like (e.g., list format, fields), error conditions, or prerequisites. For a tool in a complex server with many siblings, more context is needed to guide effective use.

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 the 'year' parameter fully documented in the schema. The description adds no additional parameter semantics beyond implying the year is used to filter makes for market value, which is already clear from the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

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 verb ('provides') and resource ('list of makes'), specifying it's for the 'market value API by year'. It distinguishes from generic 'makes' tools by mentioning the market value context, though it doesn't explicitly differentiate from similar tools like 'makes' or 'makes_2'.

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?

No guidance is provided on when to use this tool versus alternatives. With many sibling tools (e.g., 'makes', 'makes_2', 'market_value_by_vin'), the description lacks explicit when/when-not instructions or named alternatives, leaving usage context implied at best.

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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