--- title: "How to Connect Amazon SP-API and Ads API with AI Using an MCP Connector" description: "Learn how to connect Amazon SP-API and Amazon Ads API with AI using an MCP connector. Build AI assistants that can access orders, inventory, listings, advertising performance, and marketplace insights securely." slug: "connect-amazon-sp-api-and-amazon-ads-api-with-mcp" date: "2026-06-17" author: "Jayesh Jain" category: "Artificial Intelligence" tags: ["Amazon SP-API", "Amazon Ads API", "MCP", "AI Agents", "Claude AI","ChatGPT", "Ecommerce Automation", "Marketplace Operations"] keywords: "Amazon SP-API AI integration, Amazon Ads API AI integration, MCP connector for Amazon seller, Amazon MCP server, Claude Amazon integration, ChatGPT Amazon integration, Amazon AI assistant, Amazon seller AI agent, Amazon advertising AI assistant, ecommerce AI automation, seller central AI integration" excerpt: "A practical guide to connecting Amazon SP-API and Amazon Ads API with AI using an MCP connector, so seller and marketplace teams can query orders, inventory, listings, campaigns, and ad performance through approved tools." featuredImage: "/blog/amazon-sp-api-mcp-connector.png" cta: "Want to connect Amazon marketplace data with AI securely?" ctaDescription: "Tirnav can help you build a custom MCP connector for Amazon SP-API and Ads API, with the right approvals, audit trails, and automation workflows for your ecommerce operations." --- # How to Connect Amazon SP-API and Ads API with AI For serious Amazon sellers, agencies, aggregators, and ecommerce SaaS platforms, the real challenge is not access to data. It is turning disconnected operational and advertising data into fast, safe decisions. Your seller operations may already rely on the **Amazon Selling Partner API (SP-API)** for orders, listings, inventory, reports, and feeds. Your growth team may already rely on the **Amazon Ads API** for campaigns, keywords, budgets, and performance reporting. But most AI assistants cannot work with either system directly. They do not automatically know: - Which SKUs are close to stockout - Which orders are delayed or unshipped - Which listings have suppressed or incomplete content - Which campaigns are wasting spend - Which search terms are converting without enough budget - How ad performance and inventory risk affect each other - When an action should be suggested versus actually executed That gap is where an **MCP connector for Amazon SP-API and Ads API** becomes useful. In this guide, we will explain how to connect both Amazon APIs to an AI assistant using **Model Context Protocol (MCP)**. We will walk through the architecture, authorization flow, tool design, security model, and real business use cases for operations, catalog, and advertising teams. --- ## What Is Amazon SP-API? **Amazon SP-API**, or **Selling Partner API**, is Amazon's official API for seller and marketplace operations. It gives approved applications a way to work with business data and workflows such as: - Orders - Inventory - Listings - Catalog and product data - Reports - Feeds - Pricing - Fulfillment and operational workflows In simple terms, SP-API is the API layer behind many of the day-to-day workflows that Amazon sellers and marketplace operators care about. If you want an AI assistant to answer questions like: - "Which SKUs are low on stock?" - "What orders are delayed?" - "Which listings have issues?" - "Did this feed fail?" then SP-API is usually the system that provides that data. For many Amazon businesses, SP-API is the operational source of truth. --- ## What Is Amazon Ads API? **Amazon Ads API** is Amazon's official API for advertising and campaign management. It lets approved applications access and work with advertising data such as: - Campaigns - Ad groups - Keywords and targets - Budgets - Search term performance - Clicks, spend, and attributed sales - Sponsored advertising reporting In simple terms, Ads API tells you how paid acquisition is performing across your Amazon advertising accounts. If you want an AI assistant to answer questions like: - "Which campaigns are overspending?" - "Which search terms get clicks but no conversions?" - "Where should we increase budget?" - "Which products have low ad visibility?" then Amazon Ads API is the layer that provides those answers. For marketplace growth teams, this is the advertising source of truth. --- ## What Is an MCP Connector? An **MCP connector** is a bridge between an AI assistant and an external system. In this case, the external systems are: - Amazon SP-API - Amazon Ads API The connector exposes safe, approved tools that the assistant can use instead of giving the AI raw access to your APIs or credentials. For example, instead of letting an AI model freely call Amazon endpoints, you can expose tools like: - **amazon_sp_inventory_summary** - **amazon_sp_orders** - **amazon_ads_campaign_performance** - **amazon_cross_channel_sku_summary** The MCP connector handles: - Authentication - Input validation - Tool permissions - Logging - Error handling - Approval logic for risky actions That means the assistant can help with real Amazon workflows while your business still controls what it is allowed to read, suggest, or change. --- ## What Does It Mean to Connect Amazon APIs with AI? It does **not** mean dumping your entire Amazon seller account into a chatbot. The better architecture is tool-based and permission-aware: 1. Your Amazon seller app remains the system that holds credentials and business logic. 2. A local or hosted MCP server exposes approved tools to the AI assistant. 3. The MCP server calls **SP-API** for seller operations data. 4. The MCP server calls **Ads API** for advertising data. 5. The assistant can read or act only through the tools you choose to expose. In simple terms: ```text AI Assistant | v MCP Connector |--------------------> Amazon SP-API |--------------------> Amazon Ads API | v Your validation, approvals, logs, and business rules ``` This matters because Amazon workflows are not all equal. Reading ad performance is low risk. Changing campaign budgets is medium risk. Submitting listing feeds or changing fulfillment-sensitive data is high risk. MCP helps you model that difference cleanly. --- ## Why Use Both SP-API and Ads API Together? Many teams integrate only one side of Amazon and miss the bigger picture. ### SP-API answers operational questions like: - What orders came in today? - Which SKUs are running low? - What is the current listing status? - Which reports are ready for download? - Which feeds succeeded or failed? ### Ads API answers growth questions like: - Which campaigns are spending the most? - Which keywords are driving conversions? - Which products are under-advertised? - Which campaigns have poor ROAS or ACOS? - Which budgets are capping strong-performing campaigns? When you combine them through AI, you unlock much more valuable workflows: - "Pause spend on SKUs that are about to stock out." - "Show products with strong conversion but weak inventory coverage." - "Find ASINs with healthy stock and low ad visibility." - "Summarize why sales dropped: traffic, ad spend, availability, or listing issues." That is where the integration starts becoming operationally useful instead of just informational. --- ## Why MCP Is a Good Fit for Amazon AI Integrations **Model Context Protocol (MCP)** gives AI assistants a standard way to discover and call external tools. Without MCP, teams usually build custom glue code around prompts, scripts, APIs, and hidden internal functions. That works for a prototype, but it becomes hard to govern. With MCP, you can define specific tools such as: - **amazon_sp_orders** - **amazon_sp_inventory_summary** - **amazon_sp_listing_issues** - **amazon_sp_reports_request** - **amazon_ads_profiles** - **amazon_ads_campaign_performance** - **amazon_ads_search_terms** - **amazon_ads_budget_update** - **amazon_cross_channel_sku_summary** Each tool has: - A clear purpose - A typed input schema - A known permission level - A predictable output shape - A place for validation and logging That makes it much easier to let AI help with Amazon workflows without giving it unrestricted API access. --- ## What SP-API and Ads API Each Cover Before designing your MCP connector, it helps to think of the two Amazon APIs as separate capability layers. ### Amazon SP-API SP-API is the operational layer for marketplace activity. It is commonly used for: - Orders - Listings and catalog workflows - Inventory and fulfillment data - Feeds and bulk updates - Reports and settlements - Pricing and offers - Notifications and integrations For some sensitive SP-API operations, especially when personally identifiable information is involved, Amazon requires additional authorization controls such as restricted access and restricted data token handling. ### Amazon Ads API Amazon Ads API is the advertising layer. It is commonly used for: - Profiles and account scoping - Campaign management - Budget control - Keywords and targets - Search term analysis - Sponsored products and related ad reporting - Performance analytics The important architectural point is this: **SP-API knows what is happening in the marketplace. Ads API knows what is happening in paid acquisition.** An AI assistant becomes far more valuable when it can reason across both. --- ## What You Can Ask an AI Assistant After Connecting Both APIs Once your MCP connector is in place, marketplace teams can ask questions like: - "List our top delayed orders from today." - "Which SKUs have less than 10 days of inventory coverage?" - "Show suppressed listings that also have active ad spend." - "Which campaigns had high spend but low attributed sales yesterday?" - "Which ASINs have enough stock but almost no ad visibility?" - "Summarize sales, ad spend, and inventory risk for our top 20 SKUs." - "Draft a budget increase plan for strong-performing campaigns with available stock." - "Prepare a feed update plan for titles or bullets on underperforming listings." And if you enable controlled write tools: - "Increase budget by 15% for profitable campaigns on in-stock hero SKUs." - "Create a listing update feed draft for products missing bullet points." - "Pause campaigns for products that are out of stock." The assistant is not acting on magic. It is calling approved MCP tools that you define and govern. --- ## Prerequisites Before you connect Amazon SP-API and Ads API with AI, you need a few pieces in place. ### 1. Amazon Selling Partner API Access You need an Amazon developer application for **SP-API** and authorization to access the seller account you want to work with. Depending on your setup, this could be: - A private app for your own organization - A public app authorized by multiple seller accounts - A partner or SaaS integration app ### 2. Amazon Ads API Access You also need an **Amazon Ads API** application with advertiser authorization and access to the correct advertising profiles. In practice, a single business may operate multiple ad profiles across marketplaces, so profile selection becomes important. ### 3. OAuth and Token Handling Both integrations depend on token-based authorization. Your connector should manage: - Secure storage of client credentials - Refresh token handling - Access token refresh - Tenant or account scoping - Expiry-aware retries ### 4. Marketplace and Region Awareness Amazon sellers often operate across regions and marketplaces. Your connector should be explicit about: - Region endpoints - Marketplace IDs - Seller account scope - Ads profile IDs - Timezone and date ranges ### 5. An MCP-Compatible AI Client You need an AI client that can call MCP tools, such as: - Claude Code - VS Code - Internal agent platforms that support MCP - Custom AI assistants using an MCP-compatible runtime ### 6. A Secure Execution Environment For production, your MCP server should run in a controlled environment with: - Encrypted secret storage - Audit logging - Role-based permissions - Approval-gated write actions - Monitoring for failed calls and throttling --- ## Step 1: Authorize the Amazon SP-API Integration Your MCP connector should never hardcode seller access assumptions. Start by authorizing the SP-API app correctly. Typical flow: 1. Create or configure your SP-API application. 2. Authorize the application for the seller account. 3. Store the refresh token securely. 4. Exchange it for access tokens when needed. 5. Use additional restricted-data controls only where required. For private internal apps, the authorization model is usually simpler because the account owner is also the app owner. For public or multi-tenant apps, the authorization workflow needs stronger onboarding, token rotation, and seller isolation. Important design note: Not every SP-API operation has the same sensitivity. If you plan to access personally identifiable information, design for stricter controls and do not expose those tools casually to an AI assistant. --- ## Step 2: Authorize the Amazon Ads API Integration Amazon Ads API has its own authorization and account-scoping model. Typical flow: 1. Create the Ads API app. 2. Send the advertiser through the OAuth grant flow. 3. Store refresh tokens securely. 4. Refresh access tokens when needed. 5. Retrieve the advertiser's available **profile IDs**. 6. Scope every ad request to the right profile. This is one of the most common integration mistakes: teams get a token working and forget that the Ads API still needs the correct profile context for the business and marketplace they want to analyze. If your business advertises in multiple countries, your connector should make profile selection explicit rather than implicit. --- ## Step 3: Build an MCP Server That Wraps Both APIs Once both integrations are authorized, build an MCP layer that exposes focused tools instead of raw endpoints. A strong first version usually includes read-only tools like: | Tool | Purpose | |---|---| | **amazon_sp_connection_check** | Verify SP-API connectivity and account context | | **amazon_sp_orders** | List orders by status, date range, or marketplace | | **amazon_sp_inventory_summary** | Show inventory levels, coverage, and stock risk | | **amazon_sp_listing_issues** | Return suppressed, incomplete, or problematic listings | | **amazon_sp_reports_status** | Track asynchronous report requests and downloads | | **amazon_ads_profiles** | List available ad profiles | | **amazon_ads_campaign_performance** | Summarize spend, sales, clicks, and efficiency | | **amazon_ads_search_terms** | Show converting or wasteful search terms | | **amazon_cross_channel_sku_summary** | Combine catalog, stock, and ad performance per SKU | After that, you can add carefully gated write tools such as: | Tool | Purpose | |---|---| | **amazon_ads_budget_update** | Adjust campaign budgets with approval | | **amazon_ads_campaign_pause** | Pause campaigns for selected reasons | | **amazon_sp_feed_submit** | Submit listing, pricing, or inventory feed updates | | **amazon_sp_listing_patch_draft** | Prepare listing changes for review before submission | The best pattern is: - Read tools enabled first - Draft tools second - Direct write tools last --- ## Step 4: Register the MCP Server with Your AI Client Once the server is ready, connect it to your AI client the same way you would connect any other MCP integration. Example environment variables might look like this: ```text AMAZON_SPAPI_CLIENT_ID=... AMAZON_SPAPI_CLIENT_SECRET=... AMAZON_SPAPI_REFRESH_TOKEN=... AMAZON_SPAPI_REGION=na AMAZON_MARKETPLACE_IDS=ATVPDKIKX0DER AMAZON_ADS_CLIENT_ID=... AMAZON_ADS_CLIENT_SECRET=... AMAZON_ADS_REFRESH_TOKEN=... AMAZON_ADS_PROFILE_ID=... ``` If you support multiple accounts, avoid using one flat set of global variables in production. Use a proper credential store and tenant mapping layer instead. --- ## Step 5: Start with Safe Read-Only Tests Before you let the assistant change anything, confirm that read operations work consistently. Good first prompts: ```text Check Amazon SP-API connectivity and list the configured marketplaces. ``` ```text List available Amazon Ads profiles for this account. ``` ```text Show inventory risk for SKUs with less than 14 days of coverage. ``` ```text Summarize yesterday's campaign performance by profile. ``` ```text Find SKUs with active campaigns but low stock. ``` This proves that: - Authentication works - Account scoping works - Marketplaces and profiles map correctly - The tool schemas are usable in natural language workflows --- ## High-Value Use Cases for Amazon SP-API + Ads API + AI This is where the integration becomes genuinely valuable. ### 1. Inventory-Aware Advertising Assistant This is one of the strongest cross-API use cases. The assistant can combine: - Inventory and fulfillment signals from SP-API - Campaign spend and performance from Ads API Then answer questions like: - Which campaigns are promoting products that may stock out soon? - Which profitable SKUs have enough inventory but underfunded campaigns? - Which ASINs should have budgets reduced because supply is constrained? This is much better than looking at inventory and ad dashboards separately. ### 2. Order Exception and Customer Operations Assistant Using SP-API, the assistant can surface: - Late shipment risk - Unshipped orders - Returned order patterns - Marketplace-specific operational issues Then it can produce: - Daily exception summaries - Ops team action lists - Escalation queues - Natural-language briefings for account managers This use case is especially helpful for multi-marketplace operators who spend too much time exporting reports and manually reviewing issues. ### 3. Catalog and Listing Quality Assistant SP-API can help detect listing and catalog issues such as: - Suppressed listings - Incomplete content - Bulk feed failures - Variation inconsistencies - Pricing or offer issues The AI assistant can then: - Explain what changed - Group issues by brand or marketplace - Draft bulk-fix plans - Prepare feed submission payloads for review This reduces the operational load on catalog teams without allowing uncontrolled edits. ### 4. Ad Waste Detection Assistant Ads API data is perfect for AI summarization when marketers need quick diagnosis instead of raw metrics. The assistant can highlight: - High-spend campaigns with weak attributed sales - Search terms with clicks but no conversions - ASIN-targeting waste - Campaigns that have drifted beyond target ACOS - Budget capping on otherwise strong ad groups Instead of a dashboard full of columns, your team gets prioritized actions. ### 5. SKU-Level Profitability and Visibility Assistant When you join both APIs, you can create SKU-level or ASIN-level narratives such as: - "This product has strong ad conversion, healthy stock, and rising orders. Consider increasing budget." - "This product is spending heavily, has low conversion, and inventory is tight. Reduce spend and review listing quality." - "This ASIN has available stock and good ratings, but ad visibility is low in the UK marketplace." This is much closer to how operators actually think than separate siloed reports. ### 6. New Product Launch Assistant For launches, teams often need one place to monitor: - Listing readiness - Inventory availability - Campaign activation - Search term traction - Budget pacing - Marketplace performance An MCP-connected AI assistant can serve as a launch copilot: - "Is the launch set up correctly?" - "Which child ASINs are not receiving impressions?" - "Are any new products missing ad coverage?" - "Do we have enough inventory to scale spend next week?" ### 7. Marketplace Weekly Business Review Assistant This is a high-adoption executive use case. The assistant can compile weekly summaries that blend: - Order trends - Top-selling SKUs - Inventory risk - Listing issues - Ad spend and return - Market or profile comparisons Instead of multiple analysts manually preparing the same report, the AI can draft the first version through trusted tools. --- ## Example Workflow: Find SKUs That Need Budget Reallocation User prompt: ```text Find SKUs with strong ROAS, more than 30 days of stock, and campaigns that may be budget-capped. ``` Behind the scenes: 1. The assistant calls an Ads performance tool. 2. It identifies campaigns or products with healthy efficiency. 3. It calls an SP-API inventory tool for the related SKUs. 4. It filters to products with enough stock coverage. 5. It returns a shortlist with recommended budget actions. This is exactly the type of cross-functional analysis that is slow for humans and ideal for governed AI tooling. --- ## Example Workflow: Prevent Wasted Ad Spend on Low-Stock Products User prompt: ```text Show products with active ad spend but less than 7 days of inventory left. ``` The assistant can: 1. Query inventory coverage from SP-API. 2. Query active campaign spend from Ads API. 3. Match SKUs or ASINs across both systems. 4. Produce a risk-ranked list. 5. Suggest pausing or reducing budgets for review. If you later enable write tools, a human approver can confirm the action before budgets are changed. --- ## Example Workflow: Listing Issue Triage with Ad Context User prompt: ```text List suppressed or incomplete listings that still have advertising spend this week. ``` This is a strong operational use case because it helps teams stop paying for traffic that lands on weak or broken product pages. The assistant can summarize: - Which ASINs are affected - Which marketplaces are affected - The current spend involved - Whether the issue is content-related, feed-related, or offer-related - Which items should be fixed first based on revenue or spend impact --- ## Safety Rules for Amazon AI Integrations This is where many teams cut corners. They should not. ### Separate Read, Draft, and Write Actions Do not expose all tools equally. Use three classes: 1. **Read-only tools** 2. **Draft or recommendation tools** 3. **Write tools requiring approval** Examples: - Reading campaign performance is read-only - Preparing a listing update file is a draft action - Submitting a feed or changing budgets is a write action ### Protect Sensitive SP-API Data Some SP-API workflows may involve sensitive order information or personally identifiable information. If your business truly needs those operations: - Use the required restricted authorization model - Limit access to approved roles only - Avoid exposing sensitive fields unless necessary - Log every access event For many AI use cases, you do not need PII at all. Design accordingly. ### Never Expose Raw Credentials to the Assistant The AI should call tools, not read secrets. Credentials, refresh tokens, and profile mappings should stay inside your secure execution layer. ### Add Human Approval for Writes Budget changes, listing updates, feed submissions, and campaign pauses should not happen automatically just because the assistant suggested them. Use approval gates such as: - Human confirmation - Role-based approval - Change previews - Threshold-based policy checks ### Keep Detailed Audit Logs For every AI-initiated action, log: - User request - Tool name - Account scope - Input payload - Output or mutation result - Timestamp - Approval identity where relevant This is essential for debugging, compliance, and trust. ### Respect Rate Limits and Async Patterns Amazon APIs often involve throttling rules, burst limits, and asynchronous jobs such as reports and feeds. Your MCP server should: - Queue long-running jobs - Retry carefully - Back off on throttling - Poll status for reports or feeds - Return partial status updates when appropriate The assistant should never assume every Amazon workflow is a single synchronous API call. --- ## Common Implementation Gotchas ### 1. Mixing Up Marketplace IDs and Ads Profile IDs These are not interchangeable. SP-API typically scopes many workflows by marketplace or seller context. Ads API scopes many workflows by advertising profile. Your connector needs a reliable mapping layer. ### 2. Assuming One Seller Account Equals One Ad Profile Large sellers often operate multiple countries, brands, or profiles. Make profile choice explicit. ### 3. Underestimating Async Workflows Reports, feeds, and bulk jobs do not always complete instantly. Design your tools so the assistant can: - Request a job - Check its status - Fetch the final result later ### 4. Letting AI Trigger Writes Too Early Teams often get excited after read-only analysis starts working and then expose write tools too quickly. That is risky. Start with: - Read summaries - Draft recommendations - Preview changes - Human approval Only then enable direct updates. ### 5. Ignoring Timezones and Business Calendars Ad data, orders, settlement periods, and inventory snapshots may not line up perfectly if your connector ignores: - Marketplace timezone - Reporting lag - Business-day cutoffs - Attribution windows For weekly business reviews and campaign analysis, date handling matters a lot. ### 6. Building Tools That Are Too Generic A raw "call any endpoint" tool is tempting, but it creates risk and prompt fragility. Instead, expose focused business tools like: - low_stock_campaign_risk - suppressed_listings_with_spend - weekly_marketplace_summary - high_waste_search_terms These are easier for users to trust and easier for engineers to govern. If your workflow also depends heavily on asynchronous Amazon reports, our detailed guide on [automating Amazon SP-API reports with n8n](/blog/mastering-amazon-sp-api-n8n-automation) is a useful companion to this MCP-based approach. --- ## Recommended Production Architecture A production-ready design should look like this: ```text User | v AI Assistant | v MCP Server | +-- Amazon SP-API read tools +-- Amazon Ads API read tools +-- Draft recommendation tools +-- Approval-gated write tools +-- Tenant/account resolver +-- Credential vault access +-- Audit logging +-- Rate limit handling +-- Async job polling | v Amazon SP-API + Amazon Ads API ``` Recommended production features: | Requirement | Why it matters | |---|---| | Read-only default mode | Safest first deployment | | Tenant isolation | Prevents cross-account mistakes | | Credential vault | Protects tokens and secrets | | Profile and marketplace mapping | Prevents wrong-scope queries | | Approval workflow | Reduces risk for budget or feed changes | | Audit logs | Essential for accountability | | Retry and backoff logic | Handles throttling safely | | Async job handling | Supports reports and feeds correctly | | PII minimization | Reduces compliance exposure | --- ## Should You Build or Buy This Connector? If your need is simple, such as read-only summaries for one seller account, you can build a lightweight MCP connector fairly quickly. But if your environment includes: - Multiple seller accounts - Multiple ad profiles - Agencies serving many brands - Internal approval workflows - SKU-level budget automation - Catalog update pipelines - Cross-marketplace reporting - Role-based access control then you are building more than a simple connector. You are building an operational AI integration layer. That usually needs: - Better credential management - Stronger observability - Clear account isolation - More careful tool design - Guardrails around high-impact actions --- ## Frequently Asked Questions ### Can AI connect to Amazon SP-API? Yes. An AI assistant can connect to Amazon SP-API through a secure intermediary such as an MCP connector that exposes approved tools for orders, inventory, listings, reports, and related seller workflows. ### Can AI connect to Amazon Ads API? Yes. An AI assistant can connect to the Amazon Ads API through approved MCP tools for profiles, campaigns, keywords, search terms, budgets, and performance reporting. ### Why use both APIs together? Because seller operations and advertising decisions affect each other. SP-API tells you what is happening in inventory, listings, and orders, while Ads API tells you what is happening in paid traffic and campaign efficiency. ### Is it safe to let AI change Amazon campaigns or listings? It can be safe only if you add guardrails such as approval flows, scoped permissions, previews, logging, and tenant isolation. Start with read-only use cases first. ### Do I need restricted access for all SP-API use cases? No. Restricted controls are mainly needed for specific sensitive SP-API operations, especially when personally identifiable information is involved. Many inventory, listing, and reporting workflows do not need that level of access. ### Can this work for agencies or aggregators managing many brands? Yes, but multi-account design becomes very important. You need strong tenant isolation, credential management, marketplace mapping, profile mapping, and audit logging. ### What is the best first use case? For most teams, the best first use case is a read-only assistant that combines inventory, campaign performance, and listing status into actionable SKU-level summaries. --- ## Final Thoughts Connecting Amazon SP-API and Ads API with AI is not about replacing your marketplace team. It is about giving operators, marketers, and managers a faster way to understand what is happening and act with more confidence. The safest rollout path is: 1. Start with read-only tools. 2. Add cross-API summaries for inventory, listings, and ad performance. 3. Introduce draft recommendations for campaign or catalog changes. 4. Add human approval before any write action. 5. Scale only after logging, validation, and account isolation are proven. With the right MCP connector, your Amazon business data becomes much more usable in day-to-day AI workflows without giving up control. **Want to build a secure AI assistant for Amazon marketplace operations and advertising?** Tirnav Solutions can help you design and implement a custom MCP connector for **Amazon SP-API** and **Amazon Ads API**, including account onboarding, secure tool design, approval flows, and business-specific automation. **[Book a Technical Discovery Call](https://cal.com/jain.jayesh/30min)**