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lcsc_resolve_bom

Resolve BOM rows into concrete LCSC parts using C-numbers or free-text queries, prioritizing basic-tier components and flagging extended parts with cost-impact warnings and assembly setup fees.

Instructions

Resolve a list of BOM rows to concrete LCSC parts. Each row may specify an LCSC C-number directly or a free-text query with an optional package. Hard-prefers basic-tier parts; for any row that resolves to an extended part, attaches a cost-impact warning and tallies the JLCPCB assembly setup fee.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYesBOM rows. Each row needs either 'lcsc' or 'query'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.16.0

TDQS

A4/5.0
Behavior4/5

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It transparently states that the tool hard-prefers basic-tier parts, attaches a cost-impact warning for extended parts, and tallies the JLCPCB assembly setup fee. This goes beyond a simple description and informs the agent of potential side effects and cost implications. However, it does not explicitly state whether the operation is read-only or has other side effects, though the resolution focus suggests a non-mutating operation.

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 concise, two sentences long, with no filler. It front-loads the core purpose, then efficiently covers input options and the behavioral nuance of basic-tier preference and cost warnings. Every sentence contributes meaning, and the structure is clear and scannable.

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?

The description adequately covers the tool's complexity: it handles both direct C-numbers and queries, applies a package filter, prefers basic-tier parts, and manages cost warnings and setup fees. It does not explicitly describe the return format (e.g., the structure of resolved rows), but the purpose 'resolve to concrete LCSC parts' implies the output is a list of resolved parts. Given the lack of an output schema, a more explicit return structure would improve completeness, but the description is still fairly complete for an agent to understand the tool's function and behavior.

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 schema description coverage is 100%, so each parameter (rows, qty, lcsc, query, package) is already documented. The description adds context by clarifying that each row may specify an LCSC C-number or a free-text query with an optional package, and notes that qty is passed through and not used for matching. This provides a high-level framing but does not add substantial new details beyond the schema. The baseline of 3 is appropriate given the complete schema coverage.

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 clearly states the tool's function: resolving a list of BOM rows to concrete LCSC parts. It specifies the input options (direct C-number or free-text query with package) and the key behavior of preferring basic-tier parts. This distinguishes it from siblings like lcsc_search (which likely searches for individual parts) and session_confirm_bom (which likely confirms a BOM). The verb 'resolve' and resource 'BOM rows' are specific and actionable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context on when to use the tool (to resolve BOM rows) and explains the resolution logic, but it does not explicitly contrast with alternatives like lcsc_search or state when not to use it. There is no mention of exclusions or conditional routing to other tools. The intended usage is implied but not directly contrasted.

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