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sanjibani

cox-automotive-mcp

by sanjibani

search_incentives

Find manufacturer incentives for a dealer account by providing vehicle make, model, and year. Returns a list of matching incentives.

Instructions

Search incentives by vehicle make/model/year for a dealer account.

Use when: "what Honda Civic 2024 incentives are available for dealer 1042?" Example: account_id=1042, make="Honda", model="Civic", year=2024 → flat list of matching incentives.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
makeNo
yearNo
modelNo
account_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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 for behavioral disclosure. It merely states it returns a flat list. It does not mention that the tool is read-only, any required permissions, rate limits, or data freshness. As a search tool, it is likely non-destructive, but the description fails to confirm this.

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 remarkably concise: two sentences and an example. Every word is useful, no redundancy. The example is well-structured and immediately understandable.

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

Completeness4/5

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

Given the tool's low complexity and the presence of an output schema, the description is largely complete. It covers the core functionality and provides a clear example. Minor gaps: what happens when no filters are applied (returns all incentives for account) is only implied, not stated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description adds meaning through the example, clarifying that make, model, and year are optional filters and account_id is the required dealer identifier. This compensates for the lack of schema descriptions.

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

Purpose5/5

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

Description clearly states 'Search incentives by vehicle make/model/year for a dealer account.' It uses a specific verb and resource, and the example distinguishes it from sibling tools like list_incentives by focusing on filtered search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides an explicit usage scenario ('what Honda Civic 2024 incentives are available for dealer 1042?') and a concrete example. However, it does not explicitly state when NOT to use this tool (e.g., when a full list is needed) or directly name alternative tools, though the sibling list_incentives is implied.

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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