Get detailed information about a specific flour by its slug. Returns full profile including protein, W strength, hydration curve, maturation profiles, and description.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Flour slug (e.g. 'caputo-pizzeria', 'manitoba-oro') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already establishes that this is a safe read operation. The description adds value by disclosing the shape of the response, listing protein, W strength, hydration curve, maturation profiles, and description, so the agent knows what data will come back. It does not cover error or not-found behavior, but that is secondary here.
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?
One compact sentence delivers the action, the required input, and the expected returns without filler. The most important detail, that the lookup is by slug, is front-loaded.
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 one-parameter read-only lookup, the description covers the input contract and enumerates the main returned data categories. It omits possible error cases or response edge cases, but nothing essential is missing for an agent to successfully invoke the tool.
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 input schema fully documents the only parameter, slug, including length constraints and concrete examples. The description adds little beyond the schema, but with 100% schema coverage the baseline of 3 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 uses a specific verb ('Get'), names the resource ('a specific flour'), and identifies the lookup key ('by its slug'). This also differentiates it from the sibling search_flours, which would be the natural choice when the slug is unknown.
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 phrase 'by its slug' gives clear context: an agent should call this only when it already has a specific flour slug. It does not explicitly mention the alternative search_flours, but the qualifying slug is enough to make the intended use case clear.
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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Each tool targets a distinct domain object or action: flour lookup/detail, oven lookup, recipe style enumeration, bake planning, active bake listing, and troubleshooting. Even search_flours and get_flour_details are clearly separated into discovery vs. deep detail, so there is no real ambiguity.
Most tool names follow a clear verb_noun pattern such as search_flours, get_flour_details, list_recipes, and plan_bake. The bare verb 'troubleshoot' breaks the pattern slightly, but overall the naming is predictable and readable.
Seven tools is well-scoped for a baking planning domain and covers the core workflow from ingredient and equipment lookup to planning and troubleshooting. Each tool earns its place without unnecessary bloat or a too-thin surface.
The set covers the main bake-planning workflow well: find flour and oven, choose a recipe style, plan the bake, and get troubleshooting help. Minor gaps exist around managing active bakes beyond listing them and lack of a dedicated recipe detail endpoint, but agents can work around these.