carsearch-mcp

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analyze_listing_images

Run vision checks on car listing images to identify rust, damage, paint mismatch, interior defects, warning lights, modifications, tire/stance issues, and engine bay problems.

Instructions

Cache the listing's photos (sha256 + perceptual hash) and run the pluggable vision backend (checklist: rust, rockers, paint mismatch, damage, interior, warning lights, mods, tires, stance, engine bay).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "listing_id": "<listing_id>"
    },
    "name": "analyze_listing_images"
  }
}

Input Schema

Name	Required	Description	Default
`force`	No
`listing_id`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 17, 2026

TDQS

A3.5/5.0

Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses meaningful traits: photos are cached with sha256 and perceptual hash, the vision backend is pluggable, and the specific checklist of vehicle attributes is evaluated. It does not mention idempotency or failure behaviors, but the core side effects and scope are clearly surfaced.

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 compressed into one purposeful sentence that front-loads the main action and object. The parenthetical checklist adds real operational detail without becoming redundant; it is slightly dense but every element contributes.

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?

The presence of an output schema covers return values, and the description explains the processing pipeline and checklist. However, the force parameter is left unexplained in both schema and description, and no usage context is given relative to similar analysis tools. This leaves the definition adequate but not complete.

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

Parameters2/5

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

The schema has 0% parameter description coverage, so the description must clarify parameters. It implicitly ties listing_id to the listing's photos, but it never explains the force parameter, which is likely crucial for controlling cache reuse versus forced re-analysis. This is a significant gap.

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?

The description names a specific verb ('Cache'/'run') and resource ('listing's photos') and enumerates exactly what the vision backend checks (rust, rockers, paint mismatch, damage, etc.). This makes it clearly distinct from the sibling analyze_listing tool, which presumably covers broader listing analysis.

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 explains what the tool does but gives no guidance on when to choose it over analyze_listing, score_listing, or get_listing. There are no exclusions, prerequisites, or conditions stated, so an agent must infer usage solely from the tool name and behavior.

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