List the signed-in baker's scheduled and active bakes. The first call prompts the chat app to authorize a Pandough account (OAuth scope bakes:read). Needs Pro (or cancelled-Pro). Returns ids, flour, hydration, schedule, and a calculator session link.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Although readOnlyHint=true already indicates a read operation, the description adds valuable behavioral context: the first call triggers an OAuth authorization prompt, requires Pro or cancelled-Pro, and returns specific fields. These details go beyond the annotation and help the agent anticipate side effects and prerequisites.
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 core purpose. Every sentence adds necessary information: scope, authorization behavior, subscription requirement, and return contents. No filler or redundant 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?
With no output schema, the description compensates by listing the returned data (ids, flour, hydration, schedule, calculator session link). It also covers authorization and subscription prerequisites, making the tool fully usable without further lookup.
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 has zero parameters, so there is no parameter ambiguity to clarify. The description still adds relevant context about the implicit signed-in baker scope, which is the closest thing to parameter semantics for this tool.
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 ('List') and a specific resource ('the signed-in baker's scheduled and active bakes'), clearly distinguishing it from sibling tools like list_recipes or plan_bake. The scope is precise and immediately actionable.
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 gives strong context for when to call it: it lists the signed-in baker's own bakes and notes the Pro requirement and OAuth prompt. It does not explicitly name alternatives or say when not to use it, but the usage context is otherwise clear.
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 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.