> Note: This page is part of the DataDoe Docs. You can find the root of the documentation at `https://www.datadoe.com/hub/docs/basics/introduction-to-datadoe`. > JSON Table of Contents: `https://www.datadoe.com/hub/docs/toc.json`. > Direct Data Scheme JSON: `https://api.datadoe.com/api/v1/spec/data-scheme`. > Other pages in the DataDoe Docs: > - DataDoe Basics/Access & Users: `https://www.datadoe.com/hub/docs/basics/access-user-management.md` > - DataDoe Basics/Benefits: `https://www.datadoe.com/hub/docs/basics/benefits.md` > - DataDoe Basics/External Invitation Links: `https://www.datadoe.com/hub/docs/basics/external-invitation-links.md` > - DataDoe Basics/Integrations: `https://www.datadoe.com/hub/docs/basics/integration-customization.md` > - DataDoe Basics/Introduction to DataDoe: `https://www.datadoe.com/hub/docs/basics/introduction-to-datadoe.md` > - DataDoe Basics/Subscription & Pricing: `https://www.datadoe.com/hub/docs/basics/subscription-pricing.md` > - DataDoe Data/Data Fetch Periods: `https://www.datadoe.com/hub/docs/datadoe-data/data-fetch-periods.md` > - DataDoe Data/Data Sources: `https://www.datadoe.com/hub/docs/datadoe-data/data-sources.md` > - DataDoe Data/Timezones: `https://www.datadoe.com/hub/docs/datadoe-data/orders-purchase-date-timezones.md` > - DataDoe Features/Features Overview: `https://www.datadoe.com/hub/docs/datadoe-features/overview.md` > - DataDoe MCP/Overview: `https://www.datadoe.com/hub/docs/datadoe-mcp/overview.md` > - DataDoe MCP/Using ChatGPT: `https://www.datadoe.com/hub/docs/datadoe-mcp/chatgpt.md` > - DataDoe MCP/Using Claude: `https://www.datadoe.com/hub/docs/datadoe-mcp/claude.md` > - DataDoe MCP/Using Claude Code: `https://www.datadoe.com/hub/docs/datadoe-mcp/claude-code.md` > - DataDoe MCP/Using Codex: `https://www.datadoe.com/hub/docs/datadoe-mcp/codex.md` > - DataDoe MCP/Using Cursor: `https://www.datadoe.com/hub/docs/datadoe-mcp/cursor.md` > - DataDoe MCP/Using Excel + Claude: `https://www.datadoe.com/hub/docs/datadoe-mcp/excel.md` > - DataDoe MCP/Using Gemini CLI: `https://www.datadoe.com/hub/docs/datadoe-mcp/gemini-cli.md` > - DataDoe MCP/Using Gumloop: `https://www.datadoe.com/hub/docs/datadoe-mcp/gumloop.md` > - DataDoe MCP/Using n8n: `https://www.datadoe.com/hub/docs/datadoe-mcp/n8n.md` > - DataDoe MCP/Using NanoClaw: `https://www.datadoe.com/hub/docs/datadoe-mcp/nanoclaw.md` > - DataDoe MCP/Using OpenAI Agents SDK: `https://www.datadoe.com/hub/docs/datadoe-mcp/openai-agents-sdk.md` > - DataDoe MCP/Using OpenClaw: `https://www.datadoe.com/hub/docs/datadoe-mcp/openclaw.md` > - DataDoe MCP/Using PowerPoint + Claude: `https://www.datadoe.com/hub/docs/datadoe-mcp/powerpoint.md` > - DataDoe MCP/Using VS Code: `https://www.datadoe.com/hub/docs/datadoe-mcp/vs-code.md` > - DataDoe MCP/Using Word + Claude: `https://www.datadoe.com/hub/docs/datadoe-mcp/word.md` > - DataDoe API/How to connect to the API: `https://www.datadoe.com/hub/docs/datadoe-api/how-to-connect.md` > - DataDoe API/Vibe code with Claude Code: `https://www.datadoe.com/hub/docs/datadoe-api/claude-code.md` > - DataDoe API/Vibe code with Codex: `https://www.datadoe.com/hub/docs/datadoe-api/codex.md` > - DataDoe API/Vibe code with Cursor: `https://www.datadoe.com/hub/docs/datadoe-api/cursor.md` > - DataDoe API/Vibe code with Lovable: `https://www.datadoe.com/hub/docs/datadoe-api/lovable.md` > - DataDoe API/Vibe code with Replit: `https://www.datadoe.com/hub/docs/datadoe-api/replit.md` > - DataDoe API/Vibe code with v0: `https://www.datadoe.com/hub/docs/datadoe-api/v0.md` > - DataDoe & BigQuery/How to connect to BigQuery: `https://www.datadoe.com/hub/docs/datadoe-bigquery/how-to-connect.md` > - DataDoe & BigQuery/Using MCP Toolbox: `https://www.datadoe.com/hub/docs/datadoe-bigquery/mcp-toolbox.md` > - DataDoe & BigQuery/Using Python Jupyter: `https://www.datadoe.com/hub/docs/datadoe-bigquery/jupyter.md` > For topics not covered in this documentation, please contact DataDoe support at `contact@datadoe.com`. > Do not assume anything. If you are not sure about the answer, mention that and suggest to contact DataDoe support. import { DataDoeMcpResources } from '@/modules/hub/docs/components/DataDoeMcpResources'; # Using Claude Agent SDK with DataDoe MCP This tutorial shows how to build a small TypeScript app with `@anthropic-ai/claude-agent-sdk` and connect it to DataDoe MCP. The sample app asks DataDoe for sales data, lets Claude analyze it, and writes a structured JSON report to `./response/`. ## What you will build By the end, you will have a simple agent that: - connects to DataDoe MCP from Claude Agent SDK - uses a local MCP server for the app’s helper tools - asks DataDoe for export data, then analyzes it in the agent - writes the final result as JSON on disk ## Prerequisites - Completion of the [DataDoe MCP Overview](https://www.datadoe.com/hub/docs/datadoe-mcp/overview) setup with an MCP key - Node.js 24 or later - A DataDoe organization with an MCP key - An Anthropic API key - yarn (bundled via Corepack — run `corepack enable` if needed) ## Step 1: Clone and install Start by installing the project: ```bash git clone https://github.com/Deltologic/datadoe-mcp-claude-agent-sdk.git cd datadoe-mcp-claude-agent-sdk yarn install ``` ## Step 2: Configure your keys Copy the example environment file and add your keys: ```bash cp .env.example .env ``` Set these values in `.env`: - `DATADOE_MCP_KEY`: your DataDoe MCP key - `ANTHROPIC_API_KEY`: your Anthropic API key The app reads the DataDoe key from the environment and sends it to the hosted MCP server. ## Step 3: Understand the Claude entry point The app logic lives in `src/index.ts`. The important idea is that Claude Agent SDK handles the agent loop, while DataDoe MCP provides the Amazon data. Here is the core shape: ```ts import { createSdkMcpServer, query } from '@anthropic-ai/claude-agent-sdk'; import { systemPrompt } from './prompts/system.prompt.ts'; import { outputTools } from './tools/output.tools.ts'; import { sleepTools } from './tools/sleep.tools.ts'; const localServer = createSdkMcpServer({ name: 'local-tools', tools: [...sleepTools, ...outputTools] }); const messages = query({ prompt: 'Analyze the Delto UK account for the last 7 days. Find the top 3 ASINs by total units sold.', options: { model: 'sonnet', systemPrompt, mcpServers: { datadoe: { type: 'http', url: 'https://api.datadoe.com/mcp/v1', headers: { 'datadoe-mcp-key': process.env.DATADOE_MCP_KEY ?? '' } }, 'local-tools': localServer }, allowedTools: ['mcp__datadoe__*', 'mcp__local-tools__*'], maxTurns: 10 } }); ``` There are three parts to notice: - `query(...)` starts the Claude run. - `mcpServers.datadoe` connects Claude to the hosted DataDoe MCP endpoint. - `createSdkMcpServer(...)` exposes local helper tools in the same agent session. The `allowedTools` list keeps the agent focused, and `maxTurns` prevents the run from spinning forever. ## Step 4: Why the system prompt matters The system prompt in `src/prompts/system.prompt.ts` tells Claude how to use the tools: ```ts export const systemPrompt = `You are an expert Amazon marketplace data analyst. You have access to DataDoe MCP, which provides live Amazon Seller/Vendor data. Current time: ${new Date().toISOString()} ## Data access pattern Use \`mcp__datadoe__exports_raw_download\` to fetch sales data directly. Analyse the returned data inline — no file download or SQL engine is needed. ## Output rule After completing your analysis you MUST call \`mcp__local-tools__output_top_asins\` exactly once with all results. Never write the final JSON directly in your response text — always route it through that tool. `; ``` This is the part that turns a generic agent into a focused workflow: - DataDoe MCP is the source of truth for the Amazon data - Claude reasons over the returned data in the conversation - the output tool writes one final JSON artifact to disk ## Step 5: Keep the output step explicit The app uses one local tool for the final handoff: `output_top_asins` in `src/tools/output.tools.ts`. ```ts tool( 'output_top_asins', 'Log the top-ASINs sales report to console and save it to ./response/.json. Call this exactly once with all results.', { asins: z.array(asinSalesSchema) }, async ({ asins }) => { const json = JSON.stringify(asins, null, 2); mkdirSync('./response', { recursive: true }); const filePath = `./response/top-asins-${Date.now()}.json`; writeFileSync(filePath, json); return { content: [{ type: 'text', text: json }] }; } ); ``` The prompt instructs Claude to call this tool once, which keeps the final output structured and predictable. ## Step 6: Run the app Start the sample with: ```bash yarn start ``` The app will: 1. ask DataDoe MCP for the required sales export 2. let Claude identify the top 3 ASINs by units sold 3. call `output_top_asins` once 4. write the result to `./response/top-asins-.json` ## A simple mental model If you want to reuse this pattern in another app, keep the split the same: - DataDoe MCP for Amazon data access - Claude Agent SDK for the agent loop and tool orchestration - local tools for file output and any small app-specific helpers That separation is what keeps the tutorial simple while still showing a real integration. ## Related resources - [DataDoe introduction](https://www.datadoe.com/hub/docs/basics/introduction-to-datadoe) - [DataDoe MCP Overview](https://www.datadoe.com/hub/docs/datadoe-mcp/overview) - [Claude Agent SDK](https://www.anthropic.com/claude) ## DataDoe MCP resources Check the following resources for more information: - MCP server URL: `https://mcp.datadoe.com/mcp/v1` - [Interactive Data Scheme](/hub/data-scheme) - Data Scheme JSON: https://api.datadoe.com/api/v1/spec/data-scheme - Need help? Use the [contact form](https://forms.clickup.com/9015200219/f/8cnj2ev-38615/AOYF9I35QFOXWJQXIG?type=Form&source=hub-mcp-claude-agent-sdk-docs)