SaaS founders wear five hats. The right MCP stack collapses them into a single AI-driven workflow: Stripe for billing intelligence, Supabase for product data, Resend for transactional email, PostHog for product analytics, and GitHub + Linear for engineering. Install these five and Claude Code becomes your second cofounder.
A technical founder (0–10 employees) building a B2B SaaS who ships code, handles billing, writes marketing, and answers support — all in the same day.
Ask Claude 'what's our MRR this month?' or 'refund customer X' without leaving the editor. Critical for founders who need billing answers in seconds, not SQL queries.
Your auth, Postgres, and storage in one MCP. Claude can query users, run migrations, and debug production data directly. Replaces 80% of manual SQL work.
Open PRs, review diffs, and merge from inside Claude Code. Most founders are solo engineers — GitHub MCP means one less tab.
Turn support tickets into Linear issues in one sentence. Claude writes the issue with the right project, labels, and priority based on the conversation.
Your knowledge base — roadmap, specs, customer research, meeting notes. Claude reads and writes Notion so you stop copy-pasting between tools.
Announce deploys, summarise customer feedback, and post weekly metrics without opening Slack. Useful even for solo founders who just run a user community.
A customer emails support at 9am saying 'my upgrade failed'. You paste the email into Claude Code. The agent queries Stripe for the failed charge, pulls the user's Supabase row to confirm plan state, checks PostHog for recent error events on that user ID, retries the charge with the correct payment method, updates the user's plan in Supabase, creates a Linear issue to investigate the initial failure, and drafts a Resend email to the customer — all in under 90 seconds, one prompt.
Typical early-stage founder saves 12–15 hours/week. The biggest wins: support triage (−4h), release notes (−2h), metrics questions (−3h), and marketing copy (−3h).
Every Stripe invoice is automatically documented in a Notion database with amount, customer, and payment status.
Receive a Slack message for every successful payment, failed charge, and subscription cancellation in real time.
Every push to main triggers a Supabase migration automatically. Schema diffs are committed and applied with zero manual steps.
A Linear issue assigned to a developer automatically creates a git branch, syncs status changes, and opens a draft PR.
Open a PR and a Vercel preview URL appears as a comment within minutes. Branches are cleaned up automatically when PRs close.
Start with Stripe, Supabase, and GitHub — those three cover 80% of your daily questions. Add Linear when you need to triage issues, Notion when your docs outgrow README, and PostHog/Resend when you start tracking retention and running lifecycle emails.
All the MCP servers themselves are free and open source. You only pay for the underlying service (Stripe takes its % on payments, Supabase has a free tier up to 500MB, etc.). There is no MCPizy subscription to install these.
The Stripe MCP exposes write tools too (refunds, subscription changes, invoice creation). You control which tools are enabled — most founders keep destructive writes behind a confirmation prompt. Start read-only for the first week, then enable writes once you trust the flow.
Zapier/n8n are trigger-based: you pre-build the automation, then events fire it. MCPs are conversation-based: you ask Claude a question and it picks the right tools on the fly. For founders, MCPs win because 80% of your work is ad-hoc investigation, not fixed pipelines.
About 35 minutes: 5 min for Stripe (paste your restricted API key), 5 min for Supabase (paste service role key), 5 min each for GitHub/Linear/Notion/Slack (OAuth flows). After that, one CLI command adds them all to Claude Code: `mcpizy install stripe supabase github linear notion slack`.
An indie hacker with a Twitter audience, a newsletter, 1–3 shipped products, and zero employees. Ships daily, markets constantly, avoids meetings.
A developer building AI agents, chatbots, or autonomous workflows. Needs search, scraping, vector storage, and LLM orchestration — all as tools the agent can call.
A DevOps / SRE / platform engineer running Kubernetes, CI/CD, observability, and on-call rotations. Lives in a terminal.
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