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compare_parts

Read-onlyIdempotent

Compare 2-5 electronic components side by side, merging pricing, stock, and datasheet summaries from multiple providers. Use specific MPNs only.

Instructions

Compare 2-5 electronic components side by side in a single call. For each part, returns merged provider data (pricing, stock, structured parameters, package) plus the cached datasheet summary if one exists, plus datasheet_status ('ready', 'extracting', or 'not_extracted').

Use this instead of calling get_part_details in a loop — it fans out provider queries in parallel and merges by MPN. For discovering candidates, use search_parts or find_alternative first; compare_parts assumes you already know which MPNs you want to compare.

Behavior:

  • Uses only cached datasheet summaries — does not trigger extraction. Call prefetch_datasheets first if you need summaries for parts that haven't been extracted yet.

  • Validates every MPN upfront. If any input is not a real part number (value, description, reference designator), the whole call is rejected with a 'rejected' map listing the offenders — other parts are not compared. Filter your list before calling.

  • If a valid MPN is not found at any provider, that part still appears in the response with an 'error' field; the other parts are compared normally.

IMPORTANT — part_numbers must be specific manufacturer part numbers (e.g. 'TPS54302DDCR', 'STM32F446RCT6') or LCSC numbers (e.g. 'C2837938'). Do NOT pass component values ('100nF', '10K'), descriptions ('buck converter'), or reference designators ('U3', 'R1').

Example: compare_parts(['TPS54302', 'LM2596', 'MP2359'])

DATASHEET STATUS VALUES:

  • 'ready' — extracted and indexed; call read_datasheet, search_datasheets, or analyze_image.

  • 'extracting' / 'in_progress' / 'queued' / 'pending' — extraction running or scheduled. Poll check_extraction_status every 5-10s until 'ready' or 'failed'. Typical time: 30s-2min.

  • 'not_extracted' — known part but datasheet hasn't been fetched yet. Trigger it via prefetch_datasheets (cheapest) or by calling read_datasheet (auto-triggers on first read).

  • 'no_source' — we couldn't find a public datasheet URL for this MPN. First, retry prefetch_datasheets in 10-30s (the URL resolver re-runs and often finds a source on the second pass). If still 'no_source', the agent can upload the PDF manually via request_datasheet_upload + confirm_datasheet_upload (see those tools). Org-uploaded datasheets are private to the org.

  • 'unsupported' — PDF exists but can't be extracted (scanned image-only, encrypted, or corrupted). Upload a clean text-based PDF via request_datasheet_upload to override.

  • 'failed' / 'error' — extraction errored. The response includes the error reason. Retry via prefetch_datasheets or escalate to support.

  • 'rejected' — input wasn't a real MPN (bare value like '100nF', description, or reference designator). Fix the input and re-call.

  • 'deduplicated' — another part in the family already has this datasheet; same content is returned under the primary MPN.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
part_numbersYes2-5 specific manufacturer part numbers or LCSC numbers to compare head-to-head. Must be real MPNs — the call is rejected upfront if any entry is a value, description, or reference designator.
Behavior5/5

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

Description adds significant behavioral context beyond annotations: explains that only cached datasheets are used (no extraction triggered), validates all MPNs upfront causing whole call rejection on bad input, and details error handling. No contradiction with annotations (readOnlyHint=true, etc.).

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?

The description is well-structured with clear sections (purpose, when to use, behavior, constraints, example, datasheet statuses). Front-loaded with key information. While lengthy, every sentence serves a purpose, so it earns a high score.

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 tool's complexity and lack of output schema, the description thoroughly explains return values (merged provider data, cached datasheet summary, datasheet_status) and provides exhaustive detail on each datasheet status. This makes the tool fully understandable for an AI agent.

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?

Schema coverage is 100%, so baseline is 3. The description adds valuable semantics: clarifies that part_numbers must be specific MPNs (not values/descriptions/reference designators), provides examples, and explains validation behavior. This justifies a score above baseline.

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 purpose: compare 2-5 electronic components side by side in a single call. It uses specific verbs and resources, and distinguishes from sibling tools like get_part_details (single part) and search_parts/find_alternative (for discovery).

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

Usage Guidelines5/5

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

Explicitly states when to use (instead of looping get_part_details), provides alternatives (search_parts, find_alternative for discovery), prerequisites (prefetch_datasheets for summaries), and important constraints (input validation, example usage).

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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