300+ Integrations: What OAuth and MCP Really Mean

An integration is a secure, permissioned connection that lets an AI read data from a tool you already use, without copying that data somewhere else. "300+ integrations" means Holka can connect to that many apps using three building blocks: OAuth (you grant access without handing over a password), APIs (the structured way each tool exposes its data), and MCP (a shared standard that keeps those connections consistent). Here is what each really means.

What an 'integration' really is under the hood

When people say an AI "integrates" with Slack or HubSpot, they usually picture a magic pipe. The reality is more mundane and more reassuring. An integration is three things working together:

• Authorization, proof that you, the account owner, allow this connection.
• A data interface, the tool's own doorway for reading and writing information.
• A protocol, the agreed format for asking questions and getting answers.

None of this requires bulk-exporting your data or storing a second copy. A well-built integration reads what it needs, when it needs it, and leaves the source of truth where it lives, inside your own tools. Holka is read-only by default, so a connection is permission to look, not permission to change.

The rest of this article walks through each layer, then shows what all of it feels like on an ordinary Tuesday.

OAuth: one-click authorization without sharing passwords

OAuth is the "Sign in with Google" flow you already use every week. It solves a specific, important problem: how do you let one app read data from another without giving it your password?

Here is the flow in plain terms:

1. You click Connect on an app like Gmail or Notion.
2. You are sent to that app's own login screen, Google's, Notion's, HubSpot's, not Holka's.
3. You review exactly what is being requested and approve it.
4. The app hands back a token: a scoped key that says "this connection may read these things."

Your password never touches the AI. Holka never sees it. The token can be limited to specific permissions and revoked at any time from the source app. And because tokens are encrypted at rest, a connection is not a loose copy of your credentials sitting in a database.

This is why OAuth is the safest way to link an assistant to your stack: you delegate narrow access instead of surrendering the keys to the whole account.

APIs: how the AI reads a tool's data

Once authorized, the AI needs a way to actually fetch information. That doorway is the API, Application Programming Interface, that virtually every serious business tool publishes.

An API is a structured menu of questions a tool agrees to answer: "list the messages in this channel," "return the deals closing this quarter," "fetch this document." The AI asks in a precise format; the tool replies with structured data it can reason over.

The catch is that every API is different. Slack's menu looks nothing like Salesforce's, which looks nothing like Stripe's. Historically, connecting an AI to ten tools meant writing and maintaining ten bespoke adapters, each with its own quirks, rate limits, and breaking changes. That fragmentation is exactly the problem the next layer was designed to fix.

MCP integrations: a standard way to connect and read live

MCP, the Model Context Protocol, is an open standard for how an AI connects to external tools and reads live data. Instead of a different custom bridge for every app, MCP defines one consistent way to describe a tool, list what it can do, and request information from it in real time.

Think of it as a universal adapter. A tool that "speaks MCP" can be understood by any MCP-capable assistant, and an assistant that speaks MCP can talk to any compliant tool. The connection is live: when you ask a question, the AI queries the current state of the tool rather than a stale nightly snapshot.

For businesses, MCP integrations matter because they turn a maintenance nightmare into a plug-in ecosystem. New tools become reachable without hand-building a new connector from scratch each time. If you want the deeper mechanics, our explainer on RAG vs. connected tools goes further.

The N×M to N+M problem MCP solves

This is the clearest way to understand why a shared standard is such a big deal.

Imagine N AI assistants and M tools. In the old world, every assistant needed a custom integration for every tool, that is N × M connectors to build and maintain. Ten assistants and fifty tools means five hundred brittle bridges.

With a shared protocol, each assistant implements MCP once, and each tool exposes an MCP interface once. Now the total work is N + M. Ten assistants plus fifty tools is sixty implementations, not five hundred.

The math is why reaching hundreds of integrations went from an unrealistic promise to something a focused product can actually deliver. The breadth comes from standardization, not from an army of engineers hand-wiring every app.

What '300+ integrations' looks like day to day

Numbers are abstract. Here is the concrete payoff. When your tools are connected, you stop being the human router who copies context between apps.

• A founder asks, "What did we agree with this customer, and did we ship it?" and gets an answer stitched from Gmail, Notion, and Linear at once. See more in use cases for founders.
• A sales rep asks, "Where did the Acme deal stall?" and Holka reads the HubSpot timeline alongside the relevant Slack thread and email.
• A support lead asks, "Are customers reporting the same bug this week?" and the answer spans Zendesk, Intercom, and GitHub.

Two things make this trustworthy rather than a party trick:

• Source attribution. Every fact is labeled with the tool it came from, so you can verify instead of guess.
• Draft-then-confirm writes. If a task involves sending something, an email, a Slack reply, Holka surfaces it as a draft that needs one-tap confirmation. Nothing leaves your account silently.

The value scales with connection count. One integration is a shortcut; a hundred is a company-wide memory that finally lives in one place. You can browse the full integrations directory to see what your own stack would unlock.

Scopes, revocation and least-privilege access

Breadth is only safe if access is tightly bounded. Three concepts keep a big integration surface from becoming a big risk.

Scopes are the specific permissions attached to a token. A read-only scope on your calendar cannot post messages or delete files, it can only read the calendar. Good integrations request the narrowest scope that does the job, which is the principle of least privilege: grant the minimum access needed, nothing more.

Revocation means you stay in control after the fact. Because OAuth access lives as a token you granted, you can withdraw it at any time from the source app's connected-apps settings. Cut the token and the connection goes dark immediately, no negotiation, no support ticket.

Read-only by default is the posture that ties it together. When an assistant reads far more than it writes, and every write is gated behind explicit confirmation, the blast radius of any single connection stays small.

This is the quiet part that matters most. Connecting an AI to your apps should feel like handing over a labeled, revocable key to one room, not the master key to the building.

Browse the tools Holka connects to

The best way to understand integrations is to look at your own stack through them. Start with the tools you touch daily, connect Slack, connect Notion, or connect Gmail, and notice how much of your day is currently spent moving context between them by hand.

OAuth handles the trust. APIs expose the data. MCP makes the whole thing a standard instead of a science project. Together they are the reason a single assistant can read across your company without ever copying your data out of the tools that hold it, and that combination, not the raw count of logos, is what actually makes "300+ integrations" useful.