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Eyalm321

jlcpcb-mcp

by Eyalm321

jlcpcb_get_component_details

Get full details for a JLCPCB part, including catalog metadata, pricing tiers, specifications, datasheet, images, and stock from assembly and retail pools to assess availability for PCBA.

Instructions

Get full details for a specific JLCPCB part: catalog metadata (manufacturer, package, category), pricing tiers, parametric specifications, datasheet, and images, plus stock from both pools — jlc_assembly_stock (catalog snapshot, authoritative for PCBA) and lcsc_retail_stock (live LCSC retail). An LCSC-retail 0 is NOT an assembly shortage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lcscYesJLCPCB/LCSC part number, e.g. 'C17976', 'C1337' (the 'C' is optional)
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses that both stock pools are returned, which is authoritative, and the meaning of a 0 retail value. This is valuable behavioral context beyond what the schema shows. It does not mention rate limits or errors, but for a read-only lookup this is sufficient.

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 one dense, well-structured sentence that front-loads the purpose and then specifies components and stock semantics. Every phrase earns its place, with no redundancy or fluff.

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

Completeness5/5

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

Given the simplicity (one parameter, no output schema, no annotations), the description is remarkably complete. It details what is included, differentiates the two stock pools, and provides an interpretative warning. An agent can confidently invoke this tool based on the description alone.

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?

The input schema has 100% coverage for the single parameter 'lcsc', including an example and note that 'C' is optional. The description does not add any additional parameter semantics, but it doesn't need to because the schema fully documents it. Baseline 3 is appropriate.

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 uses a specific verb+resource ('Get full details for a specific JLCPCB part') and enumerates the exact data categories (catalog metadata, pricing tiers, specifications, datasheet, images, stock). It clearly distinguishes itself from siblings like jlcpcb_get_component_stock and jlcpcb_get_component_pricing by packaging all details together.

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 clear context: use this tool when you need comprehensive details for a part. It also includes exclusionary guidance by distinguishing assembly stock from retail stock and warning that an LCSC retail 0 is not an assembly shortage, which helps avoid misuse. It does not explicitly name alternative tools, but the context is sufficient.

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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