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Glama
Suriya-Ravichandran

Amazon India Product Research MCP

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
research_productA

Research an Amazon India product idea: category, price band, BSR, weight, rating, demand, competition, return/gating/brand risk, beginner fit and an overall 0-100 opportunity score. Every figure is labelled Live, Estimated, Historical or Demo.

analyze_product_demandA

Estimate monthly demand, demand level, trend direction and seasonality for a product on Amazon India, and return a launch decision (Strong Opportunity to Avoid). Estimates are modelled, never measured Amazon sales data.

analyze_competitionA

Analyse the Amazon India competitive landscape for a keyword: competition level, price and rating averages, review barrier, brand dominance, listing and image quality, weak listings, and bundle / differentiation / keyword opportunities.

calculate_profitabilityB

Calculate Amazon India per-order profitability: referral fee, closing fee, FBA / Easy Ship / Self Ship fulfilment cost, GST on fees, return reserve, total cost, profit, margin, ROI, break-even price and a recommended selling price, with a beginner-friendly explanation.

search_suppliersA

Research sourcing for a product in India (Parrys, Chennai, Tamil Nadu or nationwide). Returns supplier records only when a supplier data API is configured; otherwise returns real, publicly known wholesale markets, manufacturing clusters and B2B directories plus a verification checklist. Supplier names, prices and MOQs are never invented.

analyze_reviewsB

Analyse customer reviews for a product on Amazon India: most common complaints grouped by theme with mention counts, most appreciated features, quality / packaging / size / usability problems, defects, and recommended product improvements and differentiation.

research_keywordsA

Research Amazon India keywords for a product: primary, secondary, long-tail and related keywords, search intent, keyword priority, backend search terms, and where to place each keyword across title, bullets, description and backend fields.

generate_listingA

Generate an Amazon India listing: SEO title plus alternatives, five benefit-led bullet points, product description, backend search terms, keyword placement strategy, main / lifestyle / infographic / comparison image direction, packaging advice and a compliance checklist.

calculate_revenueA

Estimate monthly and annual revenue for an Amazon India listing from units sold, a best-seller rank, or Amazon's 'X bought in past month' badge. Supply product_cost to also get monthly and annual profit. Returns a range plus the method used, never a single false-precision number.

analyze_competitorsA

Profile every competitor for a keyword on Amazon India: estimated monthly units and revenue, market share, market size and concentration. Flags which competitors are NEW sellers (low review count - proof a newcomer can rank) and which clear a minimum monthly sales bar (300 units by default), then gives an entry verdict.

analyze_purchase_signalsB

Aggregate Amazon India's 'X bought in past month' badges across a keyword: how many listings show one, total units, implied revenue, which listings clear a minimum monthly sales bar, and an overall demand verdict. The badge is Amazon's own published figure, making it the most reliable free sales signal available.

analyze_review_metricsB

Measure the review barrier for an Amazon India keyword: total, median, quartile and range of competitor review counts, rating spread, how many months it would take to match the median, which listings are beatable on reviews or rating, and how many are new sellers.

analyze_evergreenA

Decide whether a product has evergreen (year-round) demand or is seasonal / a fad, using up to 5 years of search interest. Returns an evergreen score 0-100, a verdict (Evergreen to Highly Seasonal), stability / flatness / demand-floor / growth components, and inventory guidance. Uses live Google Trends when enabled - free, no API key.

analyze_product_imagesA

Analyse product imagery on Amazon India. Pass an ASIN to pull one listing's full image gallery, or a product_name to survey image coverage across a search page. Returns image counts, thin galleries you can beat, Amazon's image requirements and a concrete seven-slot gallery plan.

find_product_opportunitiesA

Screen up to 15 product ideas at once against the beginner Amazon India criteria (₹199-₹699 price, under 500 g, 30%+ margin, non-seasonal, affordable first order) and rank them by opportunity score. Omit product_ideas to screen a built-in starter list. Use this to shortlist, then run research_product on the winners.

plan_product_launchA

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.

suggest_ppc_keywordsA

Suggest Amazon Ads (Sponsored Products) keywords for a product: each with a recommended match type (exact / phrase / broad), a suggested bid and bid range derived from your unit profit, priority, and which campaign it belongs in. Harvests terms from competitor titles, and returns negative keywords plus the break-even CPC you must never bid past.

calculate_ppc_bidsB

Calculate Amazon Ads bids from unit economics: break-even ACOS (equal to your margin), target ACOS, break-even and target CPC, a bid ladder for exact / phrase / broad / auto match types, clicks needed per order, ad cost per order and profit after ads. Pass current_cpc to check whether a bid you are already running is profitable.

plan_ppc_campaignA

Build a complete Sponsored Products plan for a product on Amazon India: a three-campaign structure (Auto discovery, Manual Exact core, Phrase/Broad expansion) with the budget split across them, default bids per campaign, keyword assignments, negative keywords, projected clicks / orders / ad sales, a weekly optimisation routine, and warnings when the margin cannot support advertising.

search_webA

Search the web for product, competitor, price and supplier research. Uses DuckDuckGo by default (free, no API key); Brave, Serper, Tavily and Google Programmable Search are supported when a key is configured. Returns live web results, not Amazon data.

scrape_amazon_searchA

Scrape live Amazon India search results for a keyword: ASIN, title, price, rating, review count, 'bought in past month' badge, image and sponsored flag for each listing. Honours robots.txt, an allowlist, a crawl delay and a page budget, and stops if Amazon serves a bot challenge. Requires BROWSER_ENABLED=true and amazon.in in BROWSER_ALLOWED_DOMAINS.

scrape_amazon_productB

Scrape one live Amazon India product page by ASIN: title, brand, price, rating, review count, best-seller ranks, weight, seller, bullet points, full image gallery and the 'bought in past month' badge, plus a sales and revenue estimate derived from them.

scraper_statusA

Check how the scraping and data layers are configured: browser enabled, allowlisted domains, robots.txt enforcement, crawl delay, page budget, Playwright availability, search provider, Google Trends status, and anything blocking a live scrape.

scrape_listing_detailsA

Scrape a complete Amazon India listing by ASIN: title, all images, bullet points, description, A+ content, video, specifications table, category path, variations, badges, coupon, seller, delivery, BSR and price/discount. Then grades the listing 0-100 against Amazon best practice and tells you exactly how to beat it.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A3.7/5.0

Scored across 24 tools

Disambiguation3/5

Several tools have overlapping responsibilities, such as analyze_competition vs analyze_competitors vs analyze_review_metrics, and scrape_amazon_product vs scrape_listing_details. However, detailed descriptions clarify their distinct focuses, reducing but not eliminating ambiguity.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with lowercase and underscores (e.g., analyze_product_demand, calculate_profitability, scrape_amazon_search). Minor plural/singular variants like analyze_competition vs analyze_competitors do not break the overall predictable scheme.

Tool Count4/5

With 24 tools, the server is comprehensive but slightly heavy. Each tool addresses a distinct aspect of product research, and the count is justified by the breadth of the domain, though it approaches the upper boundary of ideal scope.

Completeness5/5

The server covers the full product research lifecycle: demand analysis, competition, profitability, keyword research, listing creation, PPC planning, scraping, review analysis, and launch planning. It also includes a health check (scraper_status) and no critical gaps are evident for its stated purpose.

Maintenance

ActivityMaintained
ResponsivenessNo issues