Skip to main content
Glama
Suriya-Ravichandran

Amazon India Product Research MCP

plan_product_launch

Create a step-by-step Amazon India launch plan: order quantities, budget split across inventory, samples, ads, photography, timeline, and warnings before committing cash.

Instructions

Turn a product decision into a launch plan for Amazon India: how many units to order, how to split the budget across inventory, samples, photography, ads and buffer, days of stock cover, reorder trigger, affordable ad cost per order, months to recover the budget, a week-by-week timeline, and warnings before you commit cash.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNoHome & Kitchen
product_costYes
product_nameYes
total_budgetNo
weight_gramsNo
selling_priceYes
packaging_costNo
fulfillment_methodNoFBA
expected_daily_salesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool produces warnings before committing cash, indicating risk-aware behavior, and implies a calculation model (units, budget splits, timeline). It does not state assumptions or edge cases (e.g., what happens with invalid inputs), but it does signal the non-trivial nature of the planning calculation.

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 a single long sentence but well-structured, front-loading the purpose and then listing outputs. It is dense but not bloated. It could be broken into two sentences for readability, but it earns its place with specific outputs. No fluff.

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 tool's complexity (9 params, multiple calculations) and absence of annotations, the description already conveys comprehensive outputs. The presence of an output schema likely details the return structure, so not describing return fields is acceptable. Missing specifics on parameter defaults and how they influence the plan, but overall the agent can infer the intent. A 4 is justified because it covers the essential 'what' and 'why', though it leaves parameter interactions to the schema.

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

Parameters3/5

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

Schema description coverage is 0%, and there are 9 parameters. The description does not detail any specific parameter except implicitly through the output list (e.g., it mentions 'budget', 'units', etc. as outputs, not as inputs). It does not explain how inputs like category or fulfillment_method affect the calculation, or defaults like total_budget=20000. The description adds context on what the tool produces overall, but it does not compensate for the complete lack of per-parameter documentation. Baseline is 3 since no param info is given, but the schema covers names/types. Given 0% coverage in the schema, the description should have added at least one or two parameter clarifications, so 3 is fair.

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 a specific verb ('plan') plus resource ('product launch'), and enumerates the concrete outputs: order quantity, budget split, days of cover, reorder trigger, ad cost, payback months, timeline, and warnings. This fully distinguishes it from siblings like calculate_profitability or plan_ppc_campaign. No ambiguity about what the tool does.

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 implies usage before committing cash to a launch, and lists the inputs a user must provide (product decision, pricing, cost). It does not explicitly name alternatives or when NOT to use it, but the context is clear enough. It could mention that financial/planning tools like calculate_profitability are complementary rather than substitutes, but that's not required.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Suriya-Ravichandran/amazon-india-seller-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server