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AgentMade Print on Demand

Get product details

get_product
Read-onlyIdempotent

Full details for one product: colours with hex codes and product photos (images: url, view, style, alt; empty when none yet; ai_generated marks AI photos), sizes, variant ids, print placements with the print area in mm, placement presets (for example center_chest, left_chest, full_front, full_back), materials and artwork guidance.

Input Schema

NameRequiredDescriptionDefault
product_idYesProduct id from search_products, e.g. "tee-heavyweight".

Output Schema

NameRequiredDescriptionDefault
brandYes
modelYes
sizesYesSize values to pass as size.
titleYes
colorsYes
categoryYes
variantsYes
image_urlYesA product photo, or null when none is approved yet.
next_stepYesWhat the agent should do next, in plain language.
placementsYes
product_idYesUse in get_product, validate_artwork and quote.
descriptionYes
size_chart_cmYes
artwork_guidanceYes
decoration_methodYes
printed_in_regionsYesRegions where this product is printed, e.g. ["EU", "US"].
production_business_daysYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare the full safety profile (readOnly, idempotent, openWorld=false, non-destructive), so the descriptor's job is supplementary. It adds a small behavioral note ('empty when none yet', 'ai_generated marks AI photos'), but most of the text restates return-value shape that the output schema already carries.

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?

Purpose is front-loaded in the opening clause, and the field list is dense but informative. The parenthetical image sub-fields (url, view, style, alt) push it toward output-schema territory, making it slightly over-packed.

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 an output schema, complete annotations, and a fully described single parameter, the definition covers what an agent needs to call it correctly. The missing piece is usage guidance – when to reach for this vs search_products or quote – but for a simple read it is close to sufficient.

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?

Only one parameter, and schema description coverage is 100% – the schema already explains product_id with an example. The description adds no additional syntax, format, or sourcing guidance beyond what the schema provides, so baseline 3 applies.

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?

States a specific verb+resource ('Full details for one product') and goes further by enumerating the fields returned. It implicitly contrasts with the listing-oriented search_products, but never names a sibling to route the agent explicitly.

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 when-to-use guidance, prerequisites, or alternatives are given. The agent must infer that this is the detail-fetch follow-up to search_products from the parameter hint alone.

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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