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Webotee Amazon Product Research

playbook_create

Save a reusable Amazon product research workflow as a playbook. Choose a built-in template or define custom steps, set a schedule, and automate recurring market analysis.

Instructions

Save a reusable per-model research workflow (a 'playbook') the user can re-run or schedule. Provide a template_key (one of: brand_watch, new_brand_radar, replenishment_watch, arbitrage_feed, defend_my_niche, find_my_next_niche, brand_defense_daily, expansion_radar, dropship_watch, spread_hunter, map_sweep, operator_network_expose, gating_risk_guardian) with its scope, OR custom steps. scope holds the inputs every step shares (e.g. {"brand":"Nike"} or an ASIN). Use when the user says 'save this as a weekly check', 'make a playbook for ...', 'automate this research'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe playbook's label.
template_keyNoBuilt-in template to seed from.
scopeNoShared inputs for the steps, e.g. {"brand":"Nike"}.
stepsNoCustom ordered [{tool,args}] (instead of a template).
scheduleNoRun cadence (default = template's or manual).
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint=false, so the tool is a write operation. The description adds context about what the playbook consists of (template, scope, steps) and how it is structured. It does not mention overwrite behavior or permissions, but overall adds value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, front-loaded with the purpose, and includes usage hints and examples. It is slightly dense but efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While the description covers main usage, it omits constraints like mutual exclusivity of template_key and steps, and does not mention output (e.g., playbook ID). For a tool with nested objects and no output schema, more detail would be beneficial.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining the relationship between template_key and steps, the role of scope, and providing an example. It goes beyond listing enum values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool saves a reusable research workflow (playbook) that can be re-run or scheduled. It distinguishes from sibling tools like playbook_list, playbook_run_now, and playbook_schedule by focusing on creation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says when to use the tool (when user says 'save this as a weekly check', etc.) and provides context for using templates vs custom steps. However, it does not explicitly exclude scenarios or compare to all siblings.

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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