MCP - Secure AI Integration and Data Access
Bridges the gap between AI models and external systems, enabling AIs to access real-world data and perform actions while maintaining security and privacy.
What is MCP?
The Model Context Protocol (MCP) is an open standard that lets AI assistants interact with tools and data securely.
Key Benefits of MCP
Gives AI tools secure, privacy-preserving access to your data
Standardizes how AI tools connect to apps and services
Works with any AI model that implements the protocol
Runs locally on your machine - no data sent to third parties
Open protocol with permissive licensing
What is Fastn UCL?
Fastn UCL makes the Model Context Protocol practical by providing a fully hosted, secure, and scalable MCP server — so your agents can connect to apps like Slack, Jira, Gmail, Shopify, and more without writing custom glue code.
How Fastn UCL Enhances Your AI Capabilities:
True Multitenancy: Built-in support for multiple tenants with isolated environments - no need for separate instances per customer
Enterprise-grade Scalability: Handles growing customer bases efficiently without performance degradation
White-labeled Experience: Present a seamless, branded integration experience for your customers
Full Integration Control: Complete API access with centralized management
Centralized Observability: Monitor and analyze all integrations from a single dashboard
Why Fastn UCL is a Game-Changer?
Most AI agents can't do anything useful because they can't access your systems. Fastn UCL solves this by creating a secure bridge between AI and your business applications.
Without Fastn UCL:
AI can only suggest actions, requiring you to manually implement them
→ With Fastn UCL:
AI can directly take action in your systems - update records, send messages, create tickets
Fastn.ai vs Traditional Solutions
How Fastn.ai compares to traditional solutions
Multitenancy
✓ Built-in, true multitenancy
✗ Each user manages own workflow
White-labeling
✓ Full brand control
✗ Not always the case
Scalability
✓ Enterprise-ready , any number of apps under one commad layer
✗ Scales poorly across tenants
SDK / Developer Tools
✓ SDKs & APIs available
✓ Dev platform, but limited embedding
Enterprise Flexibility
✓ Dynamic schema adaptation with fallback strategies and mapping resolution
✓ Rigid static schema mapping; fragile with dynamic data
Analytics & Monitoring
✓ Centralized observability
✗ Minimal usage insights
Why Choose Fastn.ai for Your SaaS?
Built for Multi-tenant SaaS: Unlike traditional iPaaS solutions that require each end user to manage their own instance, Fastn.ai is designed specifically for SaaS companies needing to manage integrations across all their customers.
White-labeled Experience: Present a seamless, branded integration experience to your customers without third-party branding interference.
Enterprise-grade Scalability: As your customer base grows, Fastn.ai scales efficiently without the overhead of managing individual integration accounts.
Cost-effective: Pricing aligned with SaaS business models prevents integration costs from eating into your margins as you scale.
Use Case: "Send a Message" Workflow
How Fastn UCL simplifies messaging across different platforms
💼 Business Scenario:
You've built a simple messaging workflow in Fastn. Every tenant (customer) just wants to send a notification like:
"Hey, your task is overdue!"
But different customers use different messaging tools:
Customer A
Slack
Customer B
Microsoft Teams
Customer C
Multitenancy Challenges:
🔹Hardcode logic:
if customer == A → send_slack()🔹Maintain multiple workflows or agents
🔹Manually manage which customer uses which tool
It gets messy fast, especially as you scale.
With Fastn + MCP + Multitenancy:
You build one single "Send Message" workflow.
Then Fastn does the rest:
Fastn UCL automatically loads the right context
Workflow picks the correct integration based on tenant
Message gets delivered to the right channel — no extra logic needed
Behind the Scenes
Each tenant has their own context:
Your Fastn workflow uses
Fastn figures out whether it's Slack or Teams using Fastn UCL context — you don't change a thing.
Output Example
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