Almond integrations and MCP: your context, in any AI client you use

What the integration layer actually does

Almond's integration layer connects your ad platforms, analytics, CRM, Slack, email, meeting recordings and business data, then classifies, prioritizes and versions every piece of context before any agent or workflow ever sees it. The result is that your AI tools stop working from a blank slate and start working from the accumulated judgment of your team.

That distinction matters more than it sounds. Most integrations are data pipes: they move information from one place to another and let the model sort it out. Almond's integrations feed a Business Context Engine, which means the information arrives ranked by relevance, recency and business impact, not in the order it was created or the volume it was generated.

What Almond connects

Setup takes roughly four minutes. Read-only access across the following source types:

• Ad platforms: Google Ads and Meta, including account structure, campaign history, test results and performance data
• Analytics and business data: the numbers that explain why the ad account looks the way it does
• CRM: customer and pipeline data that gives campaigns commercial context, not just click data
• Slack: the decisions, debates and updates that never make it into a brief
• Email: client communications, approvals and strategy threads
• Meeting recordings and notes: the place where most strategic context actually lives, and the place most AI tools never reach
• Notion and Sheets: documentation, playbooks and ad hoc analysis your team has already built

None of this is used to train models. Every source is client-scoped and isolated at the data layer, not by prompt instruction. You can export or delete your context at any time.

Why raw connections are not enough

Every agency founder who has tried connecting every data source to a general-purpose AI tool has run into the same problem: more data made the answers worse. Fourteen recommendations with no signal about which one matters this week. Insights that are technically accurate and completely irrelevant to what the team is deciding right now.

The issue is prioritization. A throwaway Slack message and a CEO strategy call both enter the model as text. Without a layer that classifies context by business impact and recency, the model treats them with roughly equal weight.

Almond's integrations are designed around this. Everything that comes in is classified and ranked before it reaches an agent. A budget decision from Monday's leadership call outranks a month-old Slack thread about creative copy. A test result from last quarter is versioned and retained so the system can say "you ran this in March and killed it on creative, not targeting" rather than proposing the same test again.

Open protocols and MCP

Almond is built on open protocols, including support for the Model Context Protocol (MCP). MCP is an emerging standard that lets AI clients request structured context from external systems at inference time, rather than requiring users to paste background into every prompt.

In practice, this means the business context Almond captures does not have to stay inside Almond's own tools. It can be delivered to Claude, ChatGPT, Cursor, or any internal AI client your team has already built or approved, through the same MCP connection.

For performance marketing teams, that has a specific implication. If your media buyers work in Claude for copy and your growth lead runs analysis in a custom internal tool, both of them can draw on the same classified, versioned context layer without switching platforms or retyping background. The context travels with the work, not the other way around.

What MCP changes for media buyers

Before MCP-style context delivery, the workflow looked like this: open the AI client, spend time writing background about the account, ask the question, get an answer calibrated to a generic model with no account history, then verify whether the answer is actually true for this client.

With Almond's context layer available via MCP, the background is already there. The model knows which campaigns are active, which tests were killed last quarter and why, what the current KPIs are, and what leadership decided on Monday. The question becomes the starting point instead of the fifth thing you type.

One framing from Almond's brand notes captures this directly: "I spent twenty minutes typing background before I could ask the actual question" is the before state. The after state is that you ask the question and the context is already there.

How this connects to Canvas and Workflow Builder

The integration layer is the foundation, not the product. Almond builds two tools directly on top of it.

Canvas is Almond's AI visual dashboard. Because it draws on the context engine, it can answer not just what the numbers show but why they look that way, referencing the decisions and tests that shaped the account. Boards are client-ready and exportable.

Workflow Builder offers 30 prebuilt Google and Meta automations that run on the context layer. The difference from trigger-based automation is that workflows can act on judgment. A workflow will not scale spend on a campaign the team decided to sunset on Monday, because the context engine knows about that decision. That is the gap between a rule and a recommendation.

For teams that want to use the context in their own tools, the MCP integration makes that available without requiring a rebuild.

Security when context is this broad

Connecting meetings, Slack, CRM and ad accounts in one place raises legitimate questions about where that data lives and who can access it.

Almond's security model is built around a few specific commitments:

• Never used for model training. Your context is not pooled across customers or used to improve any model, Almond's or a third party's.
• Bring your own LLM. You can run agents on models your security team has already approved, so context never crosses a boundary nobody signed off on.
• Client-scoped isolation. Each client's context store is isolated at the data layer. One client's strategy cannot surface in another client's output.
• Versioned and auditable. Every piece of context is versioned, so you can answer after the fact: what did the agent know, and when did it know it.
• Encrypted in transit and at rest. Role-based access controls who can see what.

For agencies managing multiple clients through one platform, the client-scoped isolation is the part that matters most. The enforcement is at the data layer, not at the prompt layer.

Where Almond fits

Almond (almond.ai) positions itself as the World's First Agentic Brain for Autonomous Performance Marketing. The integration layer is how that positioning becomes concrete: it is the mechanism by which context from every source your team uses gets captured, classified and made available to whatever AI tools are doing the work.

If your team is already using Claude or ChatGPT for performance marketing tasks and finding that the answers require constant re-briefing, the MCP integration is the specific thing worth evaluating. It does not require switching tools. It adds a context layer underneath the tools you have.

Almond 2.0 early access is open at almond.ai. Every person who joins gets a detailed one-on-one audit of their Meta and Google ad accounts with the CEO, recorded. The waitlist requires a work email.