Nov 7, 2025
Artificial Intelligence is evolving fast. AI agents today can talk, reason, and automate tasks across multiple applications. But one thing still limits how far they can go — context.
Without context, AI feels disconnected. It forgets what happened in previous interactions and struggles to work across tools.
That’s where the Model Context Protocol (MCP) changes the game. MCP provides a standardized way for AI agents to access tools, perform actions, and exchange data — allowing them to become context-driven and capable of acting intelligently.
However, while MCP gives agents the language to talk to different tools, it doesn’t provide the memory or orchestration needed to make those tools work together seamlessly.
This is where Fastn’s Unified Context Layer (UCL) takes things further — by turning MCP into a multi-tenant orchestration and memory layer that connects over 1,000 SaaS applications in a single, context-rich environment.
Together, MCP and UCL enable smarter, context-aware AI systems that remember, act, and scale effortlessly across enterprise environments.
What the Model Context Protocol (MCP) Brings to AI
Before MCP, AI systems had to rely on custom SDKs and individual APIs for each tool — Gmail, Slack, Notion, Jira, and hundreds more. This made scaling incredibly difficult.
The Model Context Protocol fixes this by introducing a universal interface that lets AI models connect to any service through a single standard.
In simpler terms, MCP acts as a translator between AI agents and the applications they use. It lets models:
Access external tools and APIs safely
Perform standardized actions using commands
Maintain structured context between tasks
Work across apps without custom integrations
MCP turns AI from a static, prompt-based model into an active, connected system that can perform tasks within your business tools.
Why MCP Alone Isn’t Enough
While MCP provides the foundation for tool access, it doesn’t handle memory, orchestration, or multi-app context — the key ingredients for real-world intelligence.
With MCP alone:
Each task runs in isolation
Agents don’t share or retain context
There’s no orchestration layer to coordinate actions
Developers must still manage data storage, logs, and security
So while MCP defines how AI can access tools, it doesn’t manage how those tools work together.
That’s where Unified Context Layer (UCL) takes over — extending MCP into a full-fledged orchestration and context system for AI.
How Unified Context Layer (UCL) Extends MCP
Fastn’s Unified Context Layer is a multi-tenant MCP server that not only connects AI agents to external services but also adds persistent memory, orchestration, and secure governance across all of them.
Think of UCL as MCP’s smarter, enterprise-ready upgrade — built for AI systems that need both power and reliability.
1. Persistent Context Across Apps
UCL keeps context alive between sessions, tools, and agents. Whether it’s an email thread in Gmail, a ticket in Jira, or a deal in HubSpot, the AI can recall past details instantly.
2. Multi-App Orchestration
UCL coordinates actions across apps. For example, your agent can read a Slack message, check data in Notion, create a Jira issue, and update the CRM — all in one smooth workflow.
3. Unified Authentication and Governance
With tenant-based authentication, UCL enables secure, enterprise-grade deployments. Each workspace is isolated, logged, and fully auditable.
4. Schema-Aware Commands
Developers can define input and output schemas for every tool. That means more control, better validation, and safer AI execution across connected services.
5. Real-Time Logging and Observability
UCL tracks every action, providing complete transparency for debugging and compliance — essential for enterprise reliability.
Why Context Is the Heart of AI Intelligence
MCP helps AI talk to tools but context helps AI make sense of what it’s doing. Without a memory of what’s happened before, even the most advanced AI agent acts blindly. UCL adds this missing layer of intelligence.
It allows agents to understand the relationships between tools, track progress across tasks, and remember what’s already been done — turning reactive systems into proactive, context-aware agents.
Without UCL | With UCL |
|---|---|
Forgets interactions | Remembers across tools and sessions |
Single-task automation | Multi-step orchestration |
Tool-specific actions | Cross-platform workflows |
Manual integrations | Single command endpoint |
Context transforms AI from answering questions to executing strategy.
Real-World Use Cases of MCP + UCL
Example 1: Sales Workflow Automation
An AI sales assistant can:
Pull leads from HubSpot
Check the communication history in Gmail
Summarize deals in Notion
Send Slack updates automatically
MCP connects these tools, while UCL gives the assistant memory and orchestration — it understands who’s been contacted, what’s pending, and what needs follow-up.
Example 2: Product Operations Agent
A product AI can:
Collect user feedback from Slack
Log bugs in Jira
Create summaries in Notion
Notify the team in Gmail
The result is a single agent that manages entire product cycles — thanks to UCL coordinating context across systems via MCP.
Example 3: Customer Support AI
A support bot can:
Fetch customer details from HubSpot
Review past tickets in Zendesk
Check refund info in Shopify
Send personalized updates
Instead of static answers, it provides context-aware resolutions, saving human teams hours daily.
Why RAG and Plugins Can’t Replace Context Layers
RAG (Retrieval-Augmented Generation) and plugins help AI access external data, but they lack continuity and orchestration. They can find answers — but they can’t follow through on actions or remember past steps.
Approach | Strength | Limitation |
|---|---|---|
RAG | Retrieves knowledge | No memory or actions |
Plugins | Adds functionality | No shared context |
MCP | Standardizes access | No orchestration |
UCL | Orchestrates and remembers | Complete context layer |
UCL doesn’t compete with RAG or MCP — it complements them, providing the persistent foundation AI agents need to truly operate like humans.
How Unified Context Layer Works Behind the Scenes
Fastn’s UCL acts as a multi-tenant MCP server, managing:
Authentication for workspaces
Persistent state and context storage
Schema validation for tool inputs and outputs
Detailed event logging
Secure command orchestration across tools
Once an AI system connects to UCL’s /command endpoint, it can interact with hundreds of apps using standardized commands — without rewriting integrations or handling complex OAuth setups.
It’s build once, use everywhere.
Who Benefits from UCL’s MCP-Based Design
AI Developers: Faster, simpler integrations with built-in orchestration.
Enterprises: Centralized governance, access control, and context sharing.
Startups: Scale AI systems without infrastructure complexity.
Operations Teams: Automate workflows across apps securely.
Product Teams: Connect feedback, data, and insights automatically.
If your AI interacts with multiple tools, UCL is the missing infrastructure piece.
The Business Impact
When MCP and UCL work together, organizations experience:
Simplified architecture: Replace dozens of connectors with one endpoint.
Improved reliability: Persistent context and error handling across apps.
Faster deployment: Launch AI agents in days, not months.
Stronger data governance: Full control and auditability for every action.
Smarter automation: Context-aware workflows that scale effortlessly.
Together, they form the backbone of a Context-Driven AI Infrastructure.
Conclusion
The Model Context Protocol redefines how AI connects to the world — it gives agents the structure to act.
The Unified Context Layer builds on top of that structure, adding memory, orchestration, and governance — the elements that make AI truly intelligent.
UCL is not just an integration layer — it’s the context brain that powers next-generation AI systems.
If MCP is how AI learns to talk, UCL is how AI learns to think.
Ready to power your AI with context that scales? 👉 Visit Fastn.ai
