Skip to main content
Glama

amazon-product-research-mcp

playbook_create

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

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already cover the safety profile (readOnly=false, destructive=false), so the description does not need to restate mutation risk. It adds persistence/reusability semantics ('re-run or schedule'), but does not disclose overwrite behavior, conflict handling, or permission requirements. With annotation coverage, this is acceptable 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.

Conciseness4/5

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

The description is front-loaded with purpose and ends with concrete usage triggers. However, it duplicates the full 13-item template_key enum that already exists in the schema, adding length without much new information for an agent.

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

Completeness4/5

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

Given the 100% schema coverage and no output schema, the description supplies the missing usage context: when to save a playbook, how to choose template vs custom steps, and how scope works. It omits explicit mention that name is required, but that is fully documented in the schema.

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% and each parameter already has a description. The tool description adds value by explaining the relationship between template_key and scope, presenting custom steps as an alternative, and giving a concrete scope example. This goes beyond rehashing the schema.

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 uses a specific verb and object ('Save a reusable per-model research workflow') and clearly identifies the playbook resource. The template/custom-step distinction and trigger phrases make its role obvious and distinguish it from siblings like playbook_list, playbook_run_now, and playbook_schedule.

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 gives explicit trigger phrases ('save this as a weekly check', 'make a playbook for ...', 'automate this research') and explains the template-versus-custom-steps choice. It does not explicitly state exclusions or name alternatives, but the usage context is clear enough for agent selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.5/5.0
Disambiguation2/5

The tool set is extremely granular, with multiple clusters that overlap in purpose (e.g., amazon_search_results/search_products/shopping_search; watchlist_delta/watchlist_diff; find_undercompeted_brands/category_undercompeted_brands; operator_new_brands/operator_new_on_brand). Although descriptions are detailed, the boundaries between many 'find opportunity' and 'watchlist change' tools are subtle enough that an agent could easily misselect.

Naming Consistency4/5

The vast majority follow a verb_noun snake_case convention with clear prefixes (asin_, brand_, category_, operator_, watchlist_, playbook_, find_, top_). A few noun-style exceptions (competitive_landscape, risk_assessment, brand_under_attack, buybox_loss_alert) break the pattern, but they are minor and do not obscure the overall scheme.

Tool Count1/5

With 82 tools, the server is far beyond the 50+ extreme threshold. Even though the domain is broad, many tools are highly granular variants (e.g., filter_brands_by_fba_share vs filter_operators_by_fba_share; watchlist_delta vs watchlist_diff) that could be merged or parameterized, imposing a heavy cognitive and context burden on agents.

Completeness5/5

The surface is extraordinarily complete for Amazon product research: discovery, ASIN/brand/category analytics, buybox and BSR history, sourcing evaluation, risk/MAP monitoring, watchlists, playbooks, operator intelligence, cross-marketplace checks, and live refreshes. Workflows like authorized_seller_set → buybox_loss_alert and watchlist_add → watchlist_delta are fully supported, with no obvious dead ends.