Your AI agent needs live data, not yesterday CSVs
Every AI agent framework solved the call a tool problem. Almost none of them solved the call a tool that fetches live web data problem. Stekpad fills that gap with a native MCP server that runs on your machine.
Your Claude session can invoke a Stekpad recipe mid-conversation and continue reasoning with fresh rows. No cron, no CSV, no tomorrow. This is the pattern we expect to define the next wave of ops tooling.
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Beyond Cron Jobs: Why Scraping Schedules Are the Wrong Model
**Use the contrarian voice from `docs/brand-voice.md`.** Take a strong position: cron-based scraping is a cargo cult from the server-side ETL era, not a design choice appropriate for 2026 workflows. Name the problem specifically: you schedule a 6am job, the data you need arrives at 3am — or a user's Claude session needs a live answer at 2pm and the next cron run is in 4 hours. Contrast two models: batch (cron, browse.ai robots, Apify schedules) vs on-demand (MCP calls, Zapier triggers, user-initiated). Argue that the only scraping model that fits agents, sales reps, and real-time pipelines is on-demand — triggered by the thing that needs the data. Stekpad supports both, but on-demand is the default because it matches how people actually work.
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.