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Decode manufacturer ordering part numbers

datasheetvend_decode_part_number
Read-onlyIdempotent

Decode the ordering nomenclature: per-position suffix meanings (temp grade, package, speed, packaging) and, where the datasheet's ordering information supports it, the enumerated orderable_part_numbers list — the direct answer to 'which/how many orderable part numbers'. Codes the document's ordering legend lists that the decode lacks come back as deterministic_harvest_only_rules, each with its source page: text-layer evidence to check, not decoded rules. Runs DatasheetVend Fast Extraction: a page scout routes targeted model reads and the module's own deterministic readers, then the same normalization and completion checks as a dashboard job finish the result, usually in seconds; engine names the engine on every result. If Fast Extraction cannot complete the module, the slower deep engine re-runs that module alone (engine.runtime is then monolith_rescue; the result says so, and it is billed as delivered). A rescue can take several minutes, so set a client timeout of 10 minutes. Only if the deep engine also cannot deliver does the module return status FAILED with the reason.

Input Schema

NameRequiredDescriptionDefault
partNameNoOptional part number / MPN to disambiguate a multi-part datasheet.
documentPathYeshttps:// URL to the datasheet PDF. The remote /api/mcp endpoint accepts ONLY https:// URLs (it downloads them); a local filesystem path works solely in local stdio mode (npm run mcp:serve).

Output Schema

NameRequiredDescriptionDefault
engineNoWhich engine produced this result
reviewYes
statusYesThe module's completion status (for example approved, approved_with_warnings or empty), or FAILED when Fast Extraction could not extract the module
contractYesContract id, e.g. thermal_evidence_v2
warningsYesPublic engineering warnings from the extraction.
module_idYesCanonical module id
extracted_dataYesThe module's extraction after its own normalization — values with the datasheet page they came from (source_page, where one was recorded) and, where the module provides them, confidence_score and source_text_snippet.
canonical_outputYesThe module's canonical (CAD-ready normalized) output block.
engineering_summaryYesConcise human-readable engineering summary, when emitted.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior5/5

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

Beyond annotations, the description discloses substantial operational behavior: Fast Extraction routing, fallback to the deep monolith_rescue engine, engine naming in results, billing as delivered, a recommended 10-minute client timeout, and FAILED status only when both engines fail. This is rich and actionable context for an agent.

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 front-loaded with purpose and then provides operational details, each of which adds useful behavioral context. It is dense and could be structured more cleanly, but it does not waste sentences on repetition of schema or annotations.

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 presence of an output schema and rich annotations, the description covers what the agent needs to know about execution behavior, fallback, timing, and failure. It does not need to explain return values because the output schema exists.

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?

Schema description coverage is 100%, so both partName and documentPath are already documented in the schema. The description adds no additional semantics about these parameters beyond what the schema provides, making 3 the appropriate baseline.

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?

The description states a specific verb-resource pair: decode ordering nomenclature, including per-position suffix meanings and an enumerated orderable_part_numbers list. It is clear what the tool does, but it does not explicitly differentiate itself from sibling extraction tools such as datasheetvend_extract_absolute_max or datasheetvend_extract_pinout.

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?

The description explains what the tool produces and how the engines behave, but it does not say when to use this tool versus sibling extraction tools. There are no explicit when-to-use, when-not-to-use, or alternative-tool statements.

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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