Yaeris Digital Services Launches a Hosted MCP Server for AI-Powered Marketing

Yaeris Digital Services has launched a hosted MCP server, giving AI assistants like Claude direct, real-time access to a business's marketing and sales data instead of leaving them to work from static training data alone. The product is live now at MCP server, and the underlying software is also listed on Yaeris's own Yaeris Directory profile.

What is the Yaeris MCP server?

The Yaeris MCP server is a fully managed, cloud-hosted implementation of the Model Context Protocol (MCP), an open standard that lets AI models read and act on live business data rather than relying only on what they were trained on. Instead of building and maintaining that connection yourself, Yaeris runs it for you, acting as marketing middleware between an AI model and the tools a business already uses.

In practice, this means a team can ask an AI assistant a direct question about a live campaign, a customer's order history, or this week's ad spend, and get a real, current answer, rather than a generic response based on how marketing tools typically work.

What problem does this actually solve?

AI models are blind to a business's real-time data by default, and most marketing automation still runs on rigid, rule-based triggers that break the moment something changes and need constant manual updates. Running an MCP server yourself also means real infrastructure work: hosting, security, and credential management that most marketing teams have no reason to take on themselves.

Multiple marketing tools also don't naturally speak the same language to each other. A CRM, an ad platform, and a messaging tool each expose their data differently, which is exactly the translation problem Yaeris MCP is built to remove.

Who is Yaeris MCP built for?

Yaeris positions the product for five kinds of teams that all run into the same underlying problem, just from different angles:

• Digital agencies managing multiple client accounts at once
• Marketing teams working across a fragmented stack of disconnected tools
• SaaS builders and resellers who want to offer AI-powered features without building their own MCP infrastructure
• Growth operators running WhatsApp, SMS, or other multi-channel campaigns
• Businesses that want AI-powered marketing without taking on DevOps work

How does the unified integration layer work?

Yaeris MCP connects through a single endpoint to more than 190 pre-built integrations, rather than requiring a custom connection to be built for every tool a business uses. That one endpoint is what an AI model actually talks to, and Yaeris handles translating requests out to whichever underlying platform holds the data.

The integration list spans several categories a typical marketing stack touches:

• Messaging and communication: WhatsApp APIs, SMS gateways
• CRM and sales: HubSpot, Salesforce
• Ecommerce: Shopify, WooCommerce, WordPress, Wix
• Advertising: Google Ads, Meta Ads, TikTok Ads, LinkedIn Ads
• Analytics and project tools: GA4, Asana, Monday.com

The architecture is also multi-tenant, which matters specifically for agencies: client accounts stay separated from each other on the same underlying connection, rather than needing a fully separate setup per client.

Is it locked to one AI model, and how is access secured?

Yaeris MCP is AI-agnostic and designed for any MCP-compatible model, with Claude as the primary model it's built and tested against. Access itself runs through encrypted credential storage, identity propagation, and granular tool-level allowlists, so a connected AI model only has permission to reach the specific systems and actions it's actually been granted.

Underneath the connection itself, Yaeris also handles the workflow orchestration layer that a self-built integration would otherwise need to account for: retrying failed API calls, respecting each platform's own rate limits, and queuing actions so a burst of requests from an AI model doesn't overwhelm a connected tool. OAuth and direct API token integration are both supported, so a business can connect an existing account the way it normally would rather than creating a separate credential system specifically for Yaeris.

What does a real workflow look like end to end?

A concrete example makes the mechanism easier to picture. A marketing team asks an AI assistant, in plain language, to summarize which WhatsApp campaigns drove orders this week. The assistant, connected through Yaeris MCP, queries the relevant messaging and ecommerce integrations directly, pulls the actual order and campaign data behind the scenes, and returns a real answer built from current data rather than a generic explanation of how such a report would typically be built.

The same connection can also act, not just answer. If a customer completes a purchase, the assistant can be set up to send a real-time WhatsApp confirmation and a relevant follow-up offer automatically, using the live order details rather than a static message template that has to be manually edited every time the offer changes.

What can an AI assistant actually do once it's connected?

Once an AI model is connected through Yaeris MCP, it can retrieve live campaign performance, order data, and customer information directly, and trigger real actions instead of only answering questions about them. A few concrete examples Yaeris points to:

• Pulling multi-account ad data automatically to assemble a client report
• Sending real-time WhatsApp order confirmations and follow-up upsell messages
• Identifying and personalizing outreach to specific leads at scale
• Powering a support chatbot that has full context on a customer's order and support history

The interface for all of this is conversational rather than rule-configuration: a team member (or the AI model itself, acting autonomously) can ask for something in plain language instead of setting up a trigger-and-condition workflow ahead of time.

How is this different from running your own MCP server?

Because MCP is an open standard, any team could technically build and host their own server instead of using Yaeris's. The practical tradeoff is that self-hosting means owning the setup complexity, the ongoing security responsibility, the infrastructure cost, and, for agencies specifically, the added complexity of supporting multiple clients on one system. Yaeris's hosted version is built to remove all four of those, in exchange for using Yaeris's own infrastructure rather than a business's own.

This also differs from traditional marketing automation platforms, which typically require configuring fixed rules ahead of time (if a customer does X, then send Y) and re-configuring them by hand whenever the underlying logic needs to change. An AI model connected through MCP can be asked to handle a new situation directly, in plain language, without a rule first being written and deployed for that specific case.

What does Yaeris MCP cost?

Yaeris MCP runs on a credit-based, pay-per-use model rather than a fixed monthly subscription — each API call or action an AI model makes through it consumes a small number of credits. Three tiers are currently listed:

• A free tier with 100 credits, intended for testing
• A $5 tier with 500 credits, aimed at solo operators
• A $20 tier with 2,000 credits plus a 200-credit bonus, aimed at small teams

Because pricing is usage-based rather than flat, the actual monthly cost scales with how much a team actually asks an AI model to do through the connection, rather than paying the same amount regardless of usage.

What results are early users reporting?

Yaeris reports several efficiency figures from early adopters, including up to an 80% reduction in some operational tasks, a 60% reduction in article production time for content-heavy workflows, roughly 80% time savings on report preparation, and a 35% lift in lead conversion when outreach was built around a more complete, integrated dataset. These are Yaeris's own reported figures from its early user base rather than independently audited, third-party benchmarks, and results will vary by how a given team actually uses the connection.

Separately, Yaeris also cites independent research finding that even state-of-the-art large language models complete less than 55% of complex CRM tasks successfully without a structured data connection like MCP — a data point about the underlying problem MCP-style access is meant to address, not a claim about Yaeris's own product specifically.

As with any early-stage usage data, the honest way to read these figures is as a signal that the underlying approach is working for the businesses reporting them, not as a guarantee that any given team will see the same percentage improvement. The size and makeup of Yaeris's current early-user group hasn't been published alongside these figures, which is worth keeping in mind when weighing how directly they'd translate to a different team's specific workflow.

Is Yaeris MCP the right fit for every business?

Not automatically. A business with a single marketing tool and no real cross-platform reporting need may not get much value from a unified integration layer, since there's little to unify in the first place. The clearest fit is a team already juggling several disconnected tools, an agency managing more than one client account, or anyone who has personally felt the friction of manually pulling the same report together from three different dashboards every week.

It's also worth being direct about the tradeoff in the pricing model: a credit-based, pay-per-use system is efficient for light or unpredictable usage, but a team running very high call volumes constantly should model out expected monthly credit consumption before committing, the same way anyone would with any usage-based pricing.

How does the multi-tenant setup actually help an agency day to day?

For an agency, the practical benefit of multi-tenant architecture is that a single AI assistant connection doesn't need to be rebuilt for every new client that signs on. Each client's data and credentials stay separated on the same underlying account, so onboarding a new client into the AI-assisted reporting or automation workflow is a matter of connecting their existing tools, not standing up an entirely parallel system the way a fully self-hosted setup often requires.

What standard is this actually built on?

MCP itself is an open protocol, not something Yaeris invented — full technical documentation is maintained at modelcontextprotocol.io. Yaeris's contribution is the hosted, managed layer on top of that open standard: the integrations, the multi-tenant account handling, and the credential security, rather than the protocol itself.

Why did a WhatsApp and SMS automation company build an MCP server?

Yaeris Digital Services already builds multi-channel messaging and AI chatbot tools for marketing agencies and brands, listed on its own Yaeris Directory profile alongside services like WhatsApp Blasting and its A.I. GPT Chatbot. Yaeris MCP extends that same direction: rather than building one more standalone messaging tool, it gives an AI model the connective layer needed to actually act across a business's whole marketing stack, messaging included, not just inside one platform at a time.

How can a business try it?

Yaeris MCP is live now at MCP server, where the free 100-credit tier is available for testing before committing to a paid plan. Yaeris Digital Services also lists its full suite of WhatsApp, SMS, and AI automation tools, including this MCP server, on its own Yaeris Directory profile, alongside its other services and software.