Artificial Intelligence is reshaping how organizations work, collaborate, and serve customers. But if you’ve ever tried to integrate an AI tool into your Salesforce ecosystem, you’ve probably hit the same challenges: context gaps, clunky integrations, siloed data, and brittle APIs that require constant maintenance.
For Salesforce customers, especially those already using TaskRay to manage customer onboarding, implementations, or service projects, these issues feel familiar. Success depends on context: knowing who the customer is, what they purchased, and where they are in the journey. Without that context, projects stall.
This is exactly the problem Model Context Protocols (MCPs) are designed to solve for AI. And with Salesforce investing heavily into MCP support through Agentforce, the opportunity for TaskRay and Salesforce customers is enormous.
In this article, we’ll break down:
At its core, a Model Context Protocol (MCP) is a framework that allows AI tools to share context with each other and with other systems.
Instead of relying on custom integrations or brittle APIs, MCPs provide a standardized way for AI systems to understand:
Think of MCPs as a universal translator for AI. Just like Salesforce created a common CRM data model to unify sales, service, and marketing, MCPs unify how AI tools communicate.
Let’s imagine a customer onboarding project in TaskRay.
If your team only has a list of tasks (“Send welcome email,” “Schedule kickoff call”) but no visibility into the context (“Which customer? What product did they buy? What’s their go-live date?”), the project becomes guesswork.
That’s what AI looks like without MCPs: lots of capability, no context.
Now, picture TaskRay with a project template: all tasks sequenced, dependencies defined, customer data linked, reporting dashboards ready. Suddenly, your team isn’t reinventing the wheel—they’re executing with clarity.
MCPs do for AI what TaskRay templates do for project managers. They give structure, shared understanding, and repeatability across tools.
Every Salesforce admin and project leader today is under pressure to “use AI.” But here’s the truth: without MCPs, most AI initiatives stall at the pilot stage.
Here’s why MCPs are a game-changer:
Salesforce isn’t just talking about MCPs—they’re baking them directly into Agentforce, their framework for AI agents inside the CRM. Starting in 2025, Agentforce will include a native MCP client, allowing agents to instantly connect to any MCP-compliant server without extra setup. To make adoption even easier, Salesforce is also introducing pre-built MCP servers for common needs, such as MuleSoft APIs, Salesforce DX commands, and Heroku-hosted tools.
Security and governance remain front and center. Admins will have centralized control through registries of approved MCP servers, plus features like rate limiting, identity management, and audit logs—delivering the same level of trust and oversight they already expect from Salesforce.
On top of that, Salesforce is launching AgentExchange, a marketplace for MCP-compliant tools. Similar to AppExchange, it will give businesses a trusted place to discover and adopt AI extensions that plug seamlessly into their workflows.
And for those with custom or legacy systems, MuleSoft and Heroku make it possible to wrap APIs or services as MCP servers, extending compatibility without requiring expensive re-architecture.
Here’s what all this means for Salesforce and TaskRay customers:
| Value Area | Impact for Customers |
| Productivity | AI agents can coordinate work across Salesforce, TaskRay, and external tools automatically, reducing manual effort to deliver project work faster with less resourcing. |
| Faster Time to Value | Like TaskRay templates, MCP-driven AI workflows can be deployed quickly and scaled across teams to accelerate time to value. |
| Smarter Decisions | Agents pull in context from multiple systems to provide more accurate recommendations and, in some cases, automate simple and repeatable tasks. |
| Lower Integration Costs | MCPs reduce the need for brittle, one-off integrations. The MCP becomes the centralized place where raw data and actionable intelligence come together to trigger work. |
| Governance | Centralized control gives IT confidence while empowering business teams to innovate without adding cybersecurity risk. |
| Competitive Advantage | Early adopters will deliver more connected, intelligent customer experiences with a more holistic data intelligence strategy. |
If you’re a TaskRay customer already using Salesforce, you’re sitting at a powerful intersection where project management meets AI context.
Imagine this: an MCP-enabled agent notices that a customer has multiple open support cases in Salesforce. It immediately flags the onboarding project in TaskRay as “at risk” and suggests proactive steps to get ahead of potential issues.
In another scenario, AI could analyze workload data from TaskRay, combine it with pipeline forecasts in Salesforce, and recommend staffing adjustments to balance resources more effectively.
Or picture this: the moment a customer signs a new contract in Salesforce, a Model Context Protocol passes that context to an Agentforce agent, which automatically spins up the right TaskRay project template and even schedules the kickoff meeting.
In every one of these examples, the MCP is the connective tissue that gives AI agents the context they need to be truly effective. Without it, the agent is just guessing.
Here are some practical steps Salesforce + TaskRay customers can take today:
We’ve been here before. Just as Salesforce standardized CRM data and TaskRay standardized project management inside Salesforce, MCPs are standardizing how AI agents collaborate across ecosystems.
For Salesforce customers, especially those using TaskRay, this isn’t just a technology shift. It’s a strategy shift: moving from siloed AI experiments to enterprise-wide AI orchestration.
The businesses that win will be the ones that treat MCP not as a buzzword, but as the blueprint for AI success.
AI without context is like a project without a plan.
For TaskRay customers, that lesson has always been clear: templates, dependencies, and dashboards turn chaos into clarity. Now, MCPs are bringing that same clarity to the world of AI.
With Salesforce betting big on MCP and Agentforce, the opportunity is here:
The future of AI in Salesforce isn’t just about smarter models—it’s about shared context. And Model Context Protocol makes it real.
