Scale a recipe to a target number of servings. Pass servings and ingredients array. Free tier: 20 calls/day per IP.
| Name | Required | Description | Default |
|---|---|---|---|
| servings | Yes | Current servings. | |
| ingredients | Yes | List of {name, amount, unit}. | |
| target_servings | Yes | Target servings. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It adds a rate limit ('Free tier: 20 calls/day per IP') which is useful, but it does not describe whether the operation is read-only, idempotent, or what the return value looks like. The lack of these details for a seemingly pure function is a moderate gap.
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 two concise sentences, front-loaded with the main purpose, and includes a critical rate-limit note without extra filler.
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 simple scaling tool, the description covers the core inputs and a key constraint (rate limit). It does not explain the return format, but since there is no output schema, a brief note on expected output would improve completeness. Still, it performs adequately for the tool's simplicity.
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%, so the schema already documents all three parameters. The description only repeats 'Pass servings and ingredients array' without adding new semantics beyond the schema, so it adds no extra value.
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 'Scale a recipe to a target number of servings' with a specific verb and resource. It distinguishes itself from sibling tools like convert_amount and list_units, which handle conversions and unit listing.
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 provides clear context for when to use this tool (scaling a recipe) and instructs on required inputs ('Pass servings and ingredients array'). It does not explicitly mention alternatives or exclusions, but the sibling tools are obviously different in purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct operation: ingredient amount conversion, temperature conversion, unit listing, recipe scaling, and ingredient searching. There is no overlap or ambiguity between them.
All tool names follow a consistent verb_noun pattern: convert_amount, convert_temperature, list_units, scale_recipe, search_ingredients. The naming is uniform and predictable.
With 5 tools, the server is well-scoped for a baking conversion domain. Each tool provides a necessary function without unnecessary bloat or redundancy.
The tool set covers the core domain needs: conversions (amount and temperature), unit discovery, ingredient lookup, and recipe scaling. There are no obvious gaps for typical baking conversion use cases.
