Add a batch of identical bottles to your drink inventory, setting the remaining percentage for each bottle. Track your stock accurately with details like brand, volume, and category.
Add a row of identical bottles; remaining_percent applies to each bottle.
| Name | Required | Description | Default |
|---|---|---|---|
| brand | Yes | ||
| notes | No | ||
| country | No | ||
| variant | No | ||
| category | Yes | ||
| quantity | No | ||
| beverage_name | Yes | ||
| bottle_volume_ml | Yes | ||
| remaining_percent | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It does add useful behavior: adding multiple identical bottles in one row and applying remaining_percent equally to each bottle. However, it does not disclose mutation consequences, whether existing entries are checked, or what the operation returns.
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?
The description is extremely concise, front-loads the main action, and every clause earns its place by clarifying a meaningful behavior. There is no redundant wording or irrelevant 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?
For a mutation tool with nine parameters, zero schema descriptions, no annotations, and no output schema, this description is too sparse. It omits field semantics, likely value ranges for remaining_percent, and any success or side-effect information an agent would need to call it correctly and confidently.
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 0%, so the description must compensate by explaining the nine parameters. It only clarifies remaining_percent, leaving beverage_name, category, brand, bottle_volume_ml, quantity, notes, country, and variant without any semantic explanation beyond their names.
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?
The description clearly states the action ('Add a row of identical bottles') and the resource (bottles), and the verb 'Add' naturally distinguishes it from sibling tools like update_bottle, update_remaining, and remove_bottle. It is clear and specific, though it does not explicitly name or contrast any sibling tool.
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?
There is no guidance about when to use add_bottle versus alternatives such as update_remaining or seed_demo_inventory. The description implies this is the creation tool, but it never states prerequisites, exclusions, or conditions for choosing a sibling tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.