01
Web Fetch
Enter a URL and get cleaned, structured web data.
https://item.jd.com/...
Page content · Fields · Source records
Deep data acquisition
with your AI agent.
Discover, fetch, and organize data from e-commerce, social media, and the open web.
Connect local agent
Johannes Vermeer · The Astronomer, 1668 · Public domain
Connect the agent client
you already use
Claude Code
Codex
Cursor
Trae
WorkBuddy
Manus
Windsurf
Cline
Roo Code
OpenCode
Gemini CLI
GitHub Copilot
Zed01 / Two ways to fetch
Links become structured content,
questions become usable data.
One entry, two kinds of tasks. Web Fetch handles links, Deep Fetch handles questions — then carries on with discovery, collection, and structuring.
02
Deep Fetch
Enter a research question or URL, and the system keeps looking for data and delivers results.
How did coffee-machine price bands on Tmall change over the past 90 days?
Find sources → Collect → Clean → Deliver
02 / Three data paths
Find existing data first, then decide how to continue.
The system picks the best path for each task. The paths don't compete — each delivers clean, structured data with source information.
Existing data
Search the AsKlear Data database first. Phase one focuses on Chinese e-commerce data like Tmall and JD.
TmallJDStructured queries
Local collection tools
Use local bridges, browser extensions, and other tools so the agent can access and collect web content on the user's device.
Local bridgeBrowser extensionAutomated collection
Cloud service
AsKlear Data hosts collection, cleaning, scheduled updates, and large-scale jobs.
Hosted collectionScheduled updatesLarge-scale jobs
The system chooses a data path and delivers clean, structured data with source information.
Tables · Structured data · Source records03 / From scattered pages to usable data
The result isn't an answer,
it's data you can keep using.
AsKlear Data handles cleaning, structuring, and source records, so results can flow into spreadsheets, analysis pipelines, or back to your agent.
Clean noise and duplicatesUnify fields and data typesKeep source and collection timeExport or hand back to the agent
One Deep Fetch, delivered in four steps
Every step preserves state, source, and traceability.
Discover sources
Existing data, web pages, or new collection targets
Fetch & collect
Web fetch, local or cloud jobs
Clean & structure
Field unification, dedup, and type cleanup
Deliver usable data
Structured results, source records, and next steps
DELIVERABLETables, structured data, source and collection timeSee pricing
04 / FROM THE JOURNAL
Ideas for building
with better data.
Original analysis on market shifts, data methods, and the workflows that help agents make better decisions.
How to query JD.com product sales data
A reproducible method for querying JD.com sales data—starting with the decision, analytical grain, and comparison baseline rather than the tool.
8 min readJD.com GMV, units, and ASP explained
The difficult part is not the formula—it is the denominator, aggregation level, and comparison scope. Here is a method for reading growth without mislabeling it.
8 min readHow to connect JD.com sales data to an AI agent with MCP
MCP standardizes the connection; it does not guarantee a correct answer. This workflow covers secure access, tool discovery, bounded queries, and result verification.
9 min readASKLEAR DATA