Storerecord Launches MCP Server Allowing AI Agents to Access Data and Automate Document Creation

Storerecord Co., Ltd. (Headquarters: Shinjuku-ku, Tokyo; CEO: Kotaro Higuchi; hereinafter "the company") announces the launch of the 'MCP Server' for its business data platform 'Storerecord,' designed for retail and e-commerce businesses. This new feature enables customers to directly access business data stored in Storerecord from their own AI tools.

With this functionality, customers can now retrieve and analyze sales, inventory, and profit data from Storerecord using familiar AI tools such as Claude or ChatGPT. Furthermore, they can automate the creation of reports and spreadsheets directly within these AI environments. Without needing to open a dashboard, users can simply query their AI within their existing workflow to generate data-driven analyses, reports, and even deliver updates via chat tools. By using scheduling features, AI agents can automatically generate documents—such as recommended order quantities per product—and deliver them via chat tools every Monday at 9:00 AM.

Data Preparation Costs in Retail

In retail and e-commerce operations, data critical for management decisions is often scattered across multiple systems, including core systems, operational platforms, sales systems, and Excel spreadsheets. As a result, creating even a single decision-making document involves significant manual effort: collecting data from various sources and consolidating and aggregating it in Excel.

Moreover, in retail, the process doesn’t end with document creation. Many workflows follow a sequence: ① Collect data from multiple systems and create documents → ② Humans analyze the documents and make decisions → ③ Create data files (e.g., spreadsheets) to reflect decisions in systems → ④ Upload via CSV.

Daily operations such as price reductions, purchase orders, and inter-store inventory transfers each involve this chain of 'document creation → decision → spreadsheet creation → upload.' Consequently, a substantial amount of time is spent on steps other than the most critical one: human judgment and decision-making.

The newly launched MCP Server aims to enable customers’ everyday AI agents to automate these entire workflows, including subsequent steps. MCP (Model Context Protocol) is an open standard that allows AI agents to securely connect with external data and tools. By providing an MCP Server, Storerecord enables customers’ AI agents to directly access integrated business data and use it for analysis, report generation, and proposal creation.

What You Can Do with the Storerecord MCP Server

Through the Storerecord MCP Server, AI agents can comprehensively retrieve business data integrated into Storerecord. This includes sales and profitability data such as sales revenue, gross profit, contribution margin, discount rate, and cost rate; inventory data such as stock quantity, days of inventory, sell-through rate, and stagnant SKUs; and procurement and expense data. Data can be retrieved across dimensions such as brand, category, channel, and SKU, within the scope of the customer’s permissions. Since access is not limited to fixed metrics, AI agents can flexibly retrieve any data stored in Storerecord, enabling tailored analysis and document creation based on specific objectives.

By integrating this capability with customers’ everyday AI agents or workflow automation tools, routine tasks can be fully automated. Specific use cases include:

1. Automated Weekly Sales Report Generation and Distribution

Simply instruct the AI agent: 'Summarize last week’s sales, gross profit, and contribution margin, analyze factors behind performance, and report weekly.' From then on, a detailed sales report with performance analysis will be automatically delivered at a scheduled time each week.

2. Automated Recommendations for Time-Limited Sale Settings

Instruct the AI: 'Every Wednesday, create recommendations for weekend time-limited sales and send them to the chat tool.' Based on data such as inventory levels, outstanding orders, recent sales volume, and average discount rates, optimal time-limited sale proposals will be delivered weekly via chat.

3. Automated Creation of Inter-Store Inventory Transfer Lists

Tell the AI: 'Create a draft inter-store inventory transfer list every Tuesday at 9:00 AM.' The system will then automatically generate the weekly inter-store inventory transfer list.

4. Automated Purchase Recommendation Alerts

Instruct the AI: 'Based on current inventory and recent sales, create and send recommendations for SKUs to reorder and suggested quantities via chat.' The AI will then deliver a list of recommended SKUs and quantities based on inventory levels, outstanding orders, recent sales, and average discount rates.

In this way, steps ① data collection, ② document creation by AI agents, and ③ proposal generation based on documents are all automated. Humans can then focus on step ④: reviewing and executing the proposals (e.g., uploading to systems). Automating the majority of these workflows allows executives and frontline staff to dedicate more time to high-value decision-making.

Introducing the 'Context Layer' and Support Services for AI Agent Deployment

When assigning tasks to AI agents, merely providing prompts is insufficient. Success in automation depends on how effectively a company’s unique context—such as 'What is our target inventory days?', 'What criteria do we use for time-limited sales?', or 'How do we position each brand?'—is communicated to the AI agent, and how well mechanisms are established to continuously update and maintain this context.

Leveraging its experience in supporting AI-driven workflow automation, the company now offers an onboarding support service to help each customer establish their own 'context layer' and guide them toward a state where AI agents can autonomously automate tasks. Based on an assessment of the customer’s IT environment, the service supports from designing how to deliver context to AI agents to implementing actual workflow automation.

For those interested in automating operations with Storerecord and AI agents, please feel free to contact us via the URL below.

Inquiry: https://service.storerecord.jp/

Future Outlook

The company’s mission is 'to provide high-quality management to all retail businesses.' The launch of the MCP Server marks the first step in Storerecord’s evolution into a 'retail management data platform for the AI era.'

Moving forward, the company will expand support for automating operations—such as anomaly detection, action recommendations, and automatic generation of routine reports—by enabling customers to leverage Storerecord data directly from their own AI environments. Beyond being a SaaS platform for data aggregation and visualization, Storerecord aims to support every aspect of AI adoption for retail businesses.