Every scraper built before 2026 was built for cron jobs
The scrapers you use today were designed for a world where data was pulled on a schedule and consumed tomorrow. That world ended when Claude shipped the Model Context Protocol.
The cron-job era
Apify, ScrapingBee, Bright Data, Octoparse — the entire scraping industry optimized for one pattern. Schedule a job, let it run overnight, wake up to a CSV, import the CSV into your warehouse, query it tomorrow morning.
The pattern works for batch analytics. It fails for anything real-time. It fails for agents.
The agent era
Claude, ChatGPT, Cursor, and every MCP-compatible agent has a new requirement: call a tool and get fresh data in seconds. Not tomorrow. Not after the next cron run. Now.
Cron-job scrapers cannot meet this requirement. By the time you export a CSV, your agent has already moved on. The data is stale before it even arrives.
What Stekpad does differently
Stekpad is an on-demand scraper. The MCP server exposes every saved recipe as a callable tool. Your Claude session calls "scrape_amazon_prices_for_brand_X" and gets fresh rows back in two seconds. No cron, no CSV, no tomorrow.
This is not a feature. This is a category reset. The first scraper built for agents will look very different from the scrapers built for dashboards. Stekpad is our bet on what that difference looks like.
Related on Stekpad
More in this cluster
Agents Need Live Data. Most Still Don't Have It.
**Use the contrarian voice from `docs/brand-voice.md`.** The core argument: every AI agent — Claude, GPT-4o, Gemini — has a training cutoff. The web moves daily. A company changes its pricing, a person changes jobs, a product launches, a competitor drops a feature — and your agent still knows the old version. Retrieval-augmented generation helps for documents you index. It does nothing for a live LinkedIn profile, a Google Maps listing, or a competitor's pricing page that changed yesterday. Name the gap directly: agents without live web access are answering from a snapshot, not the present. Stekpad's MCP server is the minimal-friction solution: register the server, call a recipe, get a structured response from the live page in two seconds. Show three concrete examples with the exact prompts.
Build a Data Enrichment Pipeline with Claude and Stekpad
Practical walkthrough of a three-step enrichment pipeline: (1) configure the Stekpad MCP server in Claude Desktop, (2) write a Claude prompt that calls a recipe and processes the returned rows (summarize, classify, score), (3) output the enriched data to Google Sheets. Uses a concrete example: scrape a list of companies from LinkedIn, pass each to Claude for ICP scoring, write scored rows to a Sheet. Includes the exact Claude prompt template. No Python. Non-developers can follow.
MCP Explained for Growth Teams: Give Claude Live Web Data
Plain-English explanation of the Model Context Protocol for a non-developer growth audience. Covers: what MCP is (Claude's way of calling external tools), why it matters for web data (live results vs stale training data), how the Stekpad MCP server works in practice (install once, call a recipe from Claude, get structured rows back), and three concrete growth workflows (enrich a CRM, monitor competitor pricing, build a lead list). No code required in any example.
Why Every Scraper Built for Cron Is Broken for Agents
**Use the contrarian voice from `docs/brand-voice.md`.** State strong positions and name targets: Apify, Firecrawl, browse.ai — all built for scheduled batch jobs, not synchronous agent calls. Back every claim with specifics: Apify actor cold-start times, Firecrawl's server-side rendering pipeline latency, browse.ai's robot-definition paradigm. The core argument: agents need a call-and-response data layer, not a pipeline. Stekpad's browser-native architecture is the only design that matches that requirement — a Claude session calls a recipe and gets rows back in under 2 seconds from the page the user has open. No cloud proxy. No phantom credits. No cron.