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audit_find_mpn_inconsistencies

Detect ICs sharing the same library reference and comment but using different manufacturer part numbers, flagging likely BOM typos or missing alternates before purchase.

Instructions

Find groups of ICs with the same (lib_ref, comment) but DIFFERENT Manufacturer Part Numbers.

Two presumably-identical parts pointing at different MPNs is almost always a bug: either a typo / accidental override during a sub-circuit clone, or means the design genuinely wants two sources but lost the alternates table somewhere. The agent reviewing the BOM should see this before purchase.

Pattern: ports the dashboard's analyzeMpnConsistency JS heuristic to an authoritative Pascal-side audit.

Returns: Dict with {checked, violations, items[]}. Each item carries {lib_ref, comment, mpns, designators} where mpns is a comma-joined list of the conflicting part numbers and designators lists the components involved.

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.1

TDQS

A4.4/5.0
Behavior4/5

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

No annotations, so the description carries the burden; it explains the heuristic (port of dashboard analyzeMpnConsistency), the two root causes (typo/override vs. lost alternates table), and confirms it's a read-only 'Find' audit. Lacks an explicit read-only statement or note on performance, keeping it below 5.

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?

Front-loaded with the core rule, then rationale, then return shape. Slightly verbose across three blocks, but every sentence conveys actionable information (what, why, output).

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?

For a zero-parameter audit with an output schema present, the description is complete: it defines the detection rule, the significance, and the return structure, so an agent knows exactly what it will get and why to run it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Zero parameters means baseline 4; the description nonetheless clarifies the grouping keys (lib_ref, comment) and the conflicting field (MPN), adding useful conceptual meaning even though there is nothing to invoke.

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 (Find) and resource (groups of ICs with same lib_ref/comment but different MPNs), which is precise and distinguishable from the many other audit_find_* siblings by naming the exact inconsistency it detects.

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?

Gives clear context: 'The agent reviewing the BOM should see this before purchase,' establishing when the tool matters. It doesn't name explicit alternatives or exclusions, but the usage scenario is unambiguous for a 0-param audit.

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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