stock_add_toolAdd stock to your Grocy inventory by entering a product name or ID and the amount to add, reflecting new purchases in current stock levels.
Add stock for a product (e.g. after a purchase).
| Name | Required | Description | Default |
|---|---|---|---|
| amount | Yes | Quantity to add (e.g. 2.0 for two units). | |
| product | Yes | Product name (e.g. "Milk") or numeric product ID. |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.2.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It reveals only that stock increases; it does not state whether the product must already exist, which location receives the stock, whether negative amounts are accepted, or any side effects. This matches the calibration precedent for mutation tools with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence that states the action and a use-case example with no wasted words. It is appropriately sized for a simple tool, though the parenthetical adds context rather than essential behavioral detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although the tool is simple (2 required, fully documented params) and has an output schema, it is a mutating operation with zero annotations. Missing context—location semantics, behavior for unknown products, negative-amount handling—means an agent cannot fully predict the outcome of the call. The description needed to compensate for the annotation gap and does not.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both 'product' and 'amount' already documented in the schema. The description adds no parameter-level meaning beyond what the schema provides, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('add') and resource ('stock for a product'), with a clarifying example ('after a purchase'). The verb clearly separates it from inverse/move siblings like stock_consume_tool and stock_transfer_tool, though no sibling is explicitly named. Lacks the explicit differentiation required for a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The '(e.g. after a purchase)' parenthetical implies a key usage scenario, and the verb 'add' vs. siblings 'consume'/'transfer' gives implicit routing. However, it never states when not to use it, nor does it point to alternatives such as stock_transfer_tool for moving existing stock between locations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.