Connect Amazon Seller to Claude

Multi-Client Amazon Seller Health Radar

Using the Catchr MCP, analyze Amazon Seller for [List of Client Accounts] over [Reporting Period] against [Comparison Period]. For each account, review Ordered Product Sales Amount, Units Ordered, and Refund Rate and break the results down by Sales Channel and Order Status. Focus on product and collection performance across periods and channels. Rank accounts as Healthy, Watch, or Critical using [Client Targets or Historical Baseline], and show the evidence behind each status. Return a client-ready table with the account, main change, supporting fields, business implication, recommended next action, and one sentence for the next client meeting. Separate observed changes from possible explanations, respect the reporting grain, and do not claim causation when Amazon Seller only shows correlation.

Client-Ready Amazon Seller Performance Brief

Using the Catchr MCP, review Amazon Seller for [Client Account] during [Reporting Period] versus [Comparison Period]. Review Refund Rate, Buy Box Percentage, and Units Ordered B2B and break the results down by Product SKU and Product ASIN. Explain what improved, what declined, and which changes are material for orders, products, customers, revenue, refunds, discounts, and channel performance. Produce an executive summary, three evidence-backed wins, three risks, and a prioritized action plan with owner and review date. Include a compact evidence table that ties every conclusion to the returned fields. State any unavailable field, incompatible reporting level, or small-sample limitation before recommending action, and write the final recap in language an agency account manager can use with the client.

Amazon Seller Opportunity and Risk Map

Using the Catchr MCP, compare Amazon Seller across [List of Client Accounts] for [Period] using [Materiality Threshold] and [Client-Specific Targets]. For every account, review Units Ordered B2B, Sessions, and Page Views and break the results down by Product Name and Date. Investigate customer mix, repeat purchase behavior, and order value and refund, discount, inventory, and revenue concentration risks. Build an opportunity-and-risk map ranked by business relevance, confidence, and urgency. For each finding, show the account, observed change, supporting fields, plausible explanations, first validation step, and recommended client action. Keep each client separate before creating a portfolio summary, and avoid applying one account's baseline, currency, attribution rule, or reporting definition to another.

Morning Amazon Seller Priority Queue

Using the Catchr MCP, pull Amazon Seller for [List of Client Accounts] over [Recent Period] and compare it with [Baseline Period]. For each client, review Page Views, Ordered Product Sales B2B Amount, and Ordered Product Sales B2B Currency and break the results down by Order Last Update Date and Order Purchase Date. Flag changes beyond [Alert Threshold], separate high-value issues from low-volume noise, and rank the work I should handle today. Return a queue with the client, issue, exact evidence, likely impact stated cautiously, first validation step, recommended action, and a short update I can send to the client. Do not merge accounts until each result has been checked against its own target, currency, timezone, and reporting grain.

Monthly Amazon Seller Client Report

Using the Catchr MCP, create a monthly Amazon Seller report for [Client Account] covering [Current Period] versus [Previous Period]. Review Ordered Product Sales B2B Currency, Average Sales Per Order Item B2B Amount, and Average Sales Per Order Item B2B Currency and break the results down by SKU and Order Channel. Explain the most important movement in orders, products, customers, revenue, refunds, discounts, and channel performance, including what stayed stable and what needs attention. Write a plain-English report with an executive summary, a concise evidence table, three wins, three concerns, and three actions for [Next Period]. Attach an owner and success measure to each action. State the latest available date and any missing fields or intervals so the client can distinguish measured facts from assumptions or incomplete coverage.

Amazon Seller Optimization Backlog

Using the Catchr MCP, analyze Amazon Seller for [Client Account] over [Lookback Period]. Review Average Sales Per Order Item B2B Currency, Ordered Product Sales Currency, and Average Sales Per Order Item Amount and break the results down by Amazon Order ID and IS Business Order. Identify the largest opportunities related to product and collection performance across periods and channels and refund, discount, inventory, and revenue concentration risks, then create an optimization backlog ranked by expected impact, effort, and confidence. For every task, include the observed evidence, a specific action, an owner, a due date, a success metric, and a guardrail that would stop the test. Use [Minimum Volume] and [Materiality Threshold] to avoid reacting to noise, and label any recommendation that requires a field or business definition not available in the returned dataset.

Amazon Seller Product and Revenue Performance Review

Using the Catchr MCP, analyze Amazon Seller for [Account or Entity] over [Reporting Period] versus [Comparison Period]. Review Average Sales Per Order Item Amount, Average Sales Per Order Item Currency, and Average Units Per Order Item B2B and break the results down by Purchase Number Order and Order Total Amount. Concentrate on product and collection performance across periods and channels. Identify the entities and periods driving the largest change, distinguish material movement from normal variation using [Minimum Volume] and [Business Target], and explain what the available fields can and cannot prove. Return a ranked findings table with evidence, business implication, confidence, and recommended next action, followed by a short decision summary. Keep calculations at a compatible reporting grain and do not invent a metric when Amazon Seller exposes only a related signal.

Amazon Seller Customer and Order Value Analysis

Using the Catchr MCP, investigate Amazon Seller for [Account or Entity] across [Period] with a focus on customer mix, repeat purchase behavior, and order value. Review Average Units Per Order Item B2B, Average Units Per Order Item, and Buy Box Percentage B2B and break the results down by Seller Order ID and Fulfillment Channel. Compare relevant entities against [Target, Peer Group, or Historical Baseline], then locate the strongest opportunities and clearest deterioration. For every finding, show the returned values, comparison dimension, possible explanations, and next check required before action. Deliver a prioritized opportunity map with owner, action, expected signal of improvement, and review date. Treat associations as hypotheses unless the available Amazon Seller fields establish a direct relationship.

Amazon Seller Refund and Discount Risk Scan

Using the Catchr MCP, review Amazon Seller for [Account or Entity] over [Lookback Period] to assess refund, discount, inventory, and revenue concentration risks. Review Buy Box Percentage B2B, Browser Sessions B2B, and Mobile App Sessions B2B and break the results down by Product Item Price and Product Item Tax. Flag changes outside [Tolerance], rank them by materiality and confidence, and separate confirmed data observations from possible operational causes. Return an exception table with the affected entity, period, supporting fields, severity, owner, first validation step, and recommended response. End with three repeatable monitoring rules, including the threshold, minimum volume, and required field. Do not present an unusual value as proof of an error or business cause.

Amazon Seller Data Quality Audit

Using the Catchr MCP, audit Amazon Seller for [Account or All Connected Accounts] over [Period]. Review Mobile App Sessions B2B, Sessions B2B, and Browser Page Views B2B and break the results down by Promotion Discount and Ship Promotion Discount. Check completeness, unexpected nulls, duplicate identifiers at [Expected Grain], invalid values, incompatible aggregation levels, and date consistency where the returned fields support those tests. Return rule-level failure counts and a severity-ranked exception sample with account, field, observed value, expected condition, and reproducible validation step. Distinguish source-data issues from extraction or modeling issues, and do not infer uniqueness, join keys, or business meaning unless confirmed in [Data Contract or Source Documentation].

Amazon Seller Freshness and Coverage Monitor

Using the Catchr MCP, monitor Amazon Seller delivery for [Account or All Connected Accounts] across [Lookback Period]. Review Browser Page Views B2B, Mobile App Page Views B2B, and Page Views B2B and break the results down by Item Quantity and Ship Service Level. Measure date coverage, latest available record, row or entity volume, missing intervals, and field population against [Expected Schedule] and [Freshness SLA]. Flag stale extraction, flatlined volume, schema coverage changes, and gaps beyond [Absolute and Relative Thresholds]. Return an account-level health summary plus an investigation table with first affected period, supporting fields, likely failure domain stated cautiously, owner, and next validation query. Separate genuine source inactivity from a pipeline issue whenever the evidence cannot distinguish them.

Amazon Seller Drift and Reconciliation Review

Using the Catchr MCP, compare Amazon Seller across [Current Window] and [Baseline Window] for [Account or All Connected Accounts]. Review Page Views B2B, Browser Sessions, and Mobile App Sessions and break the results down by Item Status and Marketplace ID. Profile distributions, null rates, distinct-value counts, volume, and material changes at the declared reporting grain. Reconcile related fields only when their definitions and aggregation levels are compatible. Flag abrupt shifts beyond [Drift Threshold], inconsistent totals, and values that violate [Business or Data Contract Rules]. Produce a reproducible alert table with evidence, severity, probable domain, and next check, followed by a concise assessment of which analyses may be unreliable until each issue is resolved.

Replace [variables] Facts ≠ hypotheses Action + follow-up metric

Connect Amazon Seller to Claude

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How to connect Amazon Seller to Claude ?

Authorize the Amazon Seller account in Catchr

Add the Catchr MCP as a custom connector in Claude

Choose, adapt, and run a prompt.

A simple client question should not trigger an hour of data prep.

Pull another CSV

Rebuild the context

Summarize it by hand

Ask the account. Get to the explanation.

Prepare data before every question.

Connect once. Keep asking.

Twelve Amazon Seller prompts for analyzing real account data.

Amazon Seller to Claude FAQs

What Amazon Seller data can Claude analyze through Catchr?

How granular can Amazon Seller analysis be in Claude?

Can Claude compare Amazon Seller performance across periods or accounts?

Can Claude investigate unusual changes in Amazon Seller?

What types of prompts work best for Amazon Seller analysis in Claude?

Can I combine Amazon Seller with other data sources in Claude?

Can Claude compare multiple Amazon Seller accounts or entities?

What do I need to connect Amazon Seller to Claude with Catchr?

How recent is the Amazon Seller data, and how much history can Claude analyze?

Can Claude change anything in Amazon Seller through Catchr?

How much does it cost to connect marketing data to Claude with Catchr?

Can I try the Claude integration before choosing a plan?

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