Retrieve valid serving units (e.g., G, ML, OZ) for creating custom foods.
Get available serving units for custom foods
Returns the list of valid serving units (e.g. G, ML, OZ) that can be used when creating custom foods.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry behavioral disclosure. It correctly states the tool returns a list of valid units, implying a read-only, side-effect-free operation. However, it does not detail pagination, auth requirements, or whether the list is exhaustive, although for a simple lookups these are minor gaps.
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 compact and front-loaded with the main statement, followed by a clarifying detail with examples. The opening sentence closely mirrors the tool name, causing slight redundancy, but the rest earns its place.
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?
This is a simple, parameterless lookup with an output schema present, so the description is nearly complete. It conveys the return concept and its use case, which is enough for an agent to call it correctly; nothing important is missing for this level of complexity.
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?
The tool has zero parameters, and the schema coverage trivially covers them. The description adds value by explaining what the returned units are used for, even though no parameter semantics are needed. Baseline 4 for 0 params is appropriate.
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 states a specific verb ('Get') and resource ('available serving units for custom foods'), and clarifies with examples (G, ML, OZ). It is clearly distinct from sibling tools like get_custom_foods, so an agent can tell the difference without opening schemas.
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 description explicitly frames the tool as a lookup to be used 'when creating custom foods,' giving clear context for when to call it. It does not name alternative tools or exclusion conditions, but no obvious alternative exists for this resource, so the guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.