Updated on by Susanna Fagerholm
Model Context Protocol (MCP) is a structured specification designed to standardize how AI agents access and interpret contextual information. In embedded iPaaS environments, which connect SaaS applications within host software, MCP provides a logical foundation for integrating AI agents into dynamic workflows.
What is Model Context Protocol?
As businesses increasingly adopt AI systems into their tech stack amidst the AI gold rush, the need for consistent, secure, and interpretable context handling is becoming a serious infrastructure concern. Model Context Protocol (MCP), introduced to the world by Anthropic in November 2024, establishes a standard for managing contextual interactions between models, tools, and users.
MCPs provide structured information that shapes how a Large Language Model (LLM) responds. It can include live data, stored memory, or rule-based logic, helping the AI model reason within the boundaries of predefined systems, goals, memory, function availability, authentication tokens, and more. They give businesses the option to control the pool of knowledge from which LLMs pull their data. Providing much needed structure and real-time context that the AI system can work with.
How do MCPs Work?
MCP provides a way for AI agents to understand what tools are available, what actions are allowed, and what information should be remembered. It can be set up in a few different environments depending on operational needs.
MCP operates as a structured interface layer between AI agents and the systems they interact with. It defines the environment, goals, tools, and permissions available to an agent, allowing for consistent interpretation and secure action execution. The protocol is implemented as a JSON schema that carries standardized objects including prompts, tool descriptors, and scoped resources.
At runtime, an agent receives an MCP-compliant context that may include:
- Prompts, which guide the model’s behavior with task-specific instructions.
- Resources, such as documents, memory objects, or structured data used for decision-making.
- Tools, which describe executable functions the agent is allowed to invoke, complete with input/output schema and execution rules.
- Auth tokens, used to grant access to external APIs or internal systems, often scoped per session or user.
The agent then uses this context to interpret its task, interact with tools, and update its internal state. Each action or message can be logged to build a persistent execution trace, which contributes to the MCP state. This log serves both as memory and as an audit trail for human operators.
MCPs vs. APIs – What’s the difference?
APIs expose functionality in fixed, stateless ways. This typically requires the developer to manage authentication, logic, and integration. MCP, on the other hand, wraps API access in a structured context that also includes memory, goal-setting, permission scopes, and tool availability. MCP is about defining how and when an agent should act, not just what endpoint to hit.
Where APIs are functions, MCP is orchestration. It helps bridge language models with systems by providing the structure needed to reason over those functions intelligently. In a nutshell, APIs are required to provide the LLM access to perform the actions determined by the MCP.
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MCPs in Integration and Agentic Workflows
MCPs are becoming a critical component in the broader push toward agentic AI systems that act autonomously, as it establishes a standardized framework for AI agents to interact with external systems and data sources.
Agentic workflows differ from traditional task automation in one key way: they are dynamic. Instead of executing hardcoded instructions, agents interpret goals, explore available tools, and make decisions in real time based on context. This shift places significant importance on the format of the context supplied to the agent. The core components of this context include prompts, resources and tools.
With these components, MCP enables AI agents to operate in a consistent, secure, and interpretable manner, facilitating scalable and reliable integrations across diverse systems. Agents can consistently interpret their environment, query tools, and maintain memory over multiple interactions.
Consider an agent built to support customer-facing teams by accessing a company’s internal knowledge base during a live chat. This agent receives questions through Slack, evaluates user permissions, retrieves relevant product and pricing data from tools like Google Sheets, and formulates responses using an LLM. The MCP framework enables this by packaging available tools, prompts, user context, and access controls into a format the agent can interpret and act on.
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Where MCP fits in Embedded iPaaS
Embedded integration platforms (iPaaS) serve as the connective tissue between business applications by offering integration capabilities within a host product. They operate at the intersection of workflows, APIs, and data governance. With the rise of AI-first features, these platforms are becoming ideal environments for agent-based integrations.
MCP fits naturally into this context. Most embedded iPaaS platforms already manage toolchains, authentication, and data movement. MCP supports this structure in several important ways:
- Tool Access: MCP defines how tools and functions are declared and invoked. Since iPaaS platforms already catalog available actions through their connectors, it is straightforward to wrap them in MCP-compatible descriptors.
- Authentication and Security: MCP messages can include scoped authentication tokens. Embedded platforms typically manage OAuth flows and token storage, making them well suited for inserting this information into the context passed to agents.
- Auditing and State: MCP promotes a persistent state model that aligns with the audit trail features many iPaaS platforms already provide. This helps add interpretability to agent actions and assists operators in debugging and refining workflows.
In this role, the embedded iPaaS platform becomes more than a data pipeline. It serves as middleware through which AI agents discover, authenticate, and interact with business systems. MCP supplies the structure needed to make these interactions interpretable, governed by permissions, and capable of being extended.
Security Considerations
A core strength of MCP is its emphasis on secure design from the beginning. Given the sensitive nature of the data and operations agents may handle, protocols must support detailed permissions, memory boundaries, and clear audit trails.
A case in point, Microsoft has adopted and extended MCP within the Windows ecosystem, where it now supports secure agentic interactions across tools and user data. The Windows implementation focuses on binding context locally, issuing short-lived credentials, and introducing user consent checkpoints. These principles can also be applied in multi-tenant SaaS environments supported by embedded iPaaS platforms.
This security posture is especially relevant when deploying agents that interact across user boundaries or integrate with third-party APIs. By formalizing the inclusion of permission scopes and tool constraints in the MCP message format, businesses can implement guardrails that are visible to users and enforceable by systems.
Why It Matters Now
AI agents are transitioning from research experiments to enterprise-level products. As this transition accelerates, the need for infrastructure that supports reliability, auditability, and security becomes more urgent. The Model Context Protocol does not attempt to solve every challenge in agent orchestration. However, it offers a composable foundation on which other systems can be built.
For SaaS teams using embedded iPaaS platforms, this presents an opportunity to do more with the tools already in place. Instead of simply connecting applications, an embedded iPaaS can act as the execution layer for agentic workflows. These workflows benefit from consistent context formats, secure access methods, and the ability to maintain memory across interactions.
MCP may not be a universal solution, but it addresses one of the most persistent challenges in AI integration: making context portable and consistent. As agents increasingly perform actions on behalf of users across many environments, solving this problem becomes essential.
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Conclusion
The Model Context Protocol provides a practical foundation for building AI agents that are more robust, understandable, and interoperable. For teams relying on embedded iPaaS platforms, MCP offers a way to extend those systems with AI capabilities grounded in structure, permissions, and clear context.
Whether MCP becomes the industry standard is still an open question. However, its clear structure and intentional focus on context are already shaping how developers think about integrating intelligence into their systems. For teams navigating the intersection of AI and system integration, MCP is a framework worth serious consideration.
