Search the Pandough flour database. Returns flour names, protein %, W strength, hydration ranges, and types. Use this to help users find the right flour for their bake.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | Filter by tag (e.g. 'pizza', 'bread', 'pastry') | |
| query | No | Search term (name, brand, or type) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already establishes this is a safe read tool, and the description adds the concrete return payload (names, protein %, W strength, hydration ranges, types). It does not disclose search semantics such as whether query and tag combine, whether results are filtered by default, or whether there is any result limit or pagination. With annotations covering safety, this is adequate but not rich.
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?
Two concise sentences front-load the core action and return fields, then state the intended use case. There is no redundant wording or repetition of schema details, so every sentence 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?
For a simple read-only search tool with two optional parameters and no output schema, the description is mostly complete: it names the resource, the use case, and the returned fields. It could be more complete by explaining how tag and query interact or what happens when both are supplied, but the schema already documents each parameter individually.
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 describes both parameters ('tag' and 'query') with clear descriptions, and schema description coverage is 100%, so the baseline is 3. The tool description adds no extra meaning about how these parameters behave or interact, so it does not go beyond the schema.
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 identifies a specific verb and resource: 'Search the Pandough flour database.' It also enumerates the return fields (flour names, protein %, W strength, hydration ranges, types), which tells the agent what the tool produces. It does not explicitly contrast itself with the related get_flour_details sibling, so it falls just short of full differentiation.
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?
It gives a clear use context: 'Use this to help users find the right flour for their bake.' This helps an agent decide when to reach for this tool. However, it does not state when not to use it or mention alternatives like get_flour_details, so it lacks exclusion guidance.
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.