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BACH-AI-Tools

Vehicle Database MCP Server

vin_ocr

Extract VIN numbers from images of stickers, dashboards, or documents using OCR technology to identify vehicles accurately.

Instructions

This API endpoint reads a VIN number from the VIN sticker, dashboard, vehicle documents or any image. It will take an image as an input and returns the VIN number. Support: 17 digit VIN.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileNoExample value:
urlNoExample 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 states the tool 'reads' and 'returns' data, implying a read-only operation, but lacks details on error handling, rate limits, authentication needs, or performance traits (e.g., accuracy, supported image formats). The mention of 'Support: 17 digit VIN' adds some context but is insufficient for a mutation-free tool with zero annotation coverage.

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 and front-loaded, stating the core functionality in the first sentence. Both sentences earn their place by specifying the action and a key constraint ('17 digit VIN'). There's no unnecessary verbosity, making it efficient, though it could be slightly more structured for clarity.

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's moderate complexity (image-based VIN reading), no annotations, and no output schema, the description is minimally complete. It covers the basic purpose and input type but lacks details on output format, error cases, or integration context. This is adequate for a simple read tool but leaves gaps in behavioral and usage aspects, scoring as a baseline viable description.

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 already documents the two parameters ('file' and 'url') with basic descriptions. The description adds no parameter-specific semantics beyond implying image input, which is covered by the schema. This meets the baseline of 3, as the schema handles parameter documentation adequately without extra value from the description.

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: 'reads a VIN number from... any image' and 'returns the VIN number.' It specifies the verb ('reads'), resource ('VIN number'), and input type ('image'). However, it doesn't explicitly differentiate from sibling tools like 'license_plate_ocr' or 'by_vin', which handle different inputs or methods, leaving some ambiguity in sibling context.

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 minimal usage guidance: it mentions the tool is for reading VINs from images, but offers no explicit advice on when to use it versus alternatives (e.g., 'by_vin' for non-image inputs or 'license_plate_ocr' for plates). There's no mention of prerequisites, exclusions, or comparative contexts, relying solely on the stated purpose without deeper guidance.

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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