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odoo_stock_product_flip_to_storable

Flip a product from consumable to storable despite existing stock moves. Used when a GRN was recorded for a product mistakenly set as consumable. Always preview with dry_run.

Instructions

Flip a product from consumable (is_storable=false) to storable (is_storable=true) when the product ALREADY has stock.move records. Bypasses Odoo's ORM constraint 'You can not change the inventory tracking of a product that was already used' via raw SQL in an ir.actions.server. Then inserts a stock.quant DIRECTLY (not through an inventory adjustment wizard) so no duplicate SVL is created — existing SVLs stay intact. Use when a GRN/bill was recorded for a product that was mistakenly set as consu. ALWAYS use dry_run=true first to preview impact. Returns full pre/post snapshot.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
connectionNodefault
product_idYesproduct.product ID to flip
location_idYesstock.location ID where the quant will be created (usually GRN destination)
quantityYesQuantity to set as on-hand (must match existing SVL remaining_qty for consistency)
company_idYesCompany ID — scope of the operation
in_dateYesISO datetime for stock.quant.in_date (e.g. '2026-04-20 14:40:34'). Should match GRN date.
dry_runNoIf true, only preview actions (no writes). Set false to execute.
Behavior4/5

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

Describes key behaviors: bypasses ORM constraint via raw SQL, inserts stock.quant directly without duplicate SVL, existing SVLs stay intact, returns full pre/post snapshot. Since no annotations exist, description carries full burden and covers critical traits, though it could detail the snapshot structure or error conditions.

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 a focused paragraph that front-loads purpose, then explains mechanism, use case, and precaution. No unnecessary words; every sentence adds value.

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, detailed parameters, and lack of output schema, the description sufficiently covers purpose, behavior, usage, parameters, and return value. It is complete for an agent to select and invoke correctly.

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

Parameters5/5

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

Schema coverage is high (86%), and description adds extra context for parameters like location_id ('usually GRN destination'), quantity ('must match existing SVL remaining_qty'), and in_date ('Should match GRN date'). This goes beyond the schema definitions.

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 states a specific verb ('Flip') and resource ('product from consumable to storable'), and distinguishes from siblings by detailing a unique operation (bypassing ORM constraint, direct quant insertion). No sibling tool performs this action.

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?

Provides explicit usage context: 'Use when a GRN/bill was recorded for a product that was mistakenly set as consu.' Includes a mandatory precaution: 'ALWAYS use dry_run=true first to preview impact.' This gives clear guidance on when and how to use.

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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