AutoID

Search AutoID products

search_products

Search the official AutoID catalog by model, SKU or text. Use this first when the exact SKU is unknown. Results come from the first-party AutoID canonical API. Prefer exact product groups and SKUs over loosely related resources. Customer-facing RON prices are exposed under pricing.ron_display when available; prefer the inc-VAT value for Romanian storefront answers and never convert EUR yourself.

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": {
      "query": "<query>"
    },
    "name": "search_products"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`query`	Yes	Model, SKU or product search text, for example MC9300 or ZT610.

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 11, 2026

TDQS

A3.9/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose data provenance ('first-party AutoID canonical API'), a result field (pricing.ron_display), and a domain rule (prefer inc-VAT, never convert EUR), which is genuinely useful. However it says nothing about ranking, pagination behavior, or auth/rate limits, leaving real gaps.

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?

Four short sentences, front-loaded with the primary action and usage rule. The pricing/currency guidance is somewhat verbose but is substantive domain instruction rather than filler.

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?

With no output schema and no annotations, the description covers provenance, the key pricing field, and selection guidance, which is enough to call the tool correctly. Return-shape and pagination details remain unstated but are secondary for a search endpoint.

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 coverage is 50%: query is documented in the schema and the description largely restates it ('by model, SKU or text'), adding little. The limit parameter is undocumented in both places, though its name/default make it largely self-explanatory. Baseline 3 fits a partial-coverage search schema.

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?

States a specific verb and resource ('Search the official AutoID catalog by model, SKU or text') and distinguishes scope from exact-lookup siblings via 'Use this first when the exact SKU is unknown'. An agent can tell it apart from get_product/get_product_group without opening a schema.

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?

'Use this first when the exact SKU is unknown' gives a clear activation condition and implies the alternative (exact SKU lookup), plus 'prefer exact product groups and SKUs over loosely related resources' guides result selection. It stops short of naming the alternate tool explicitly or stating when-not-to-use.

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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