Current software ecosystems leverage AI systems and LLMs (Large Language Models) to automate workflows and integrate data and processes across applications. These systems face major issues with, specifically, fragmentation in workflows because agents do not share real-time context, as well as the security risk associated with those agents handling sensitive data. Model Context Protocol (MCP) servers overcome these issues by enabling instantaneous and secure sharing of context information across data sources, thus enabling scalable, agent-driven automation.

MCP servers solve these problems by enabling secure, real-time sharing of AI model context across systems. In this article, you'll learn how MCP servers differ from traditional APIs, how to implement them for scalable AI workflows, and their role in addressing key challenges.

What is MCP?

The Model Context Protocol (MCP) facilitates real-time, synchronized sharing of structured, versioned AI model context across data sources. Unlike traditional APIs (REST or GraphQL), which operate in isolation, MCP allows agents to access and update the most current data from multiple systems, ensuring real-time decision-making. For instance, in a banking app, an AI agent queries real-time account balance, transaction history, and loan status from various interconnected MCP hosts via MCP.

Differences between traditional API and MCP

Some core features that set MCP apart include:

• A uniform interface for accessing and updating model context across systems
• Scoped authorization and dynamic client registration, ensuring agents only interact with authorized data
• Event-driven context updates with full auditability, enabling real-time updates and transparency in context changes
• Fine-grained identity and permission granularity, ensuring control over what data agents can access and interact with

By embracing MCP, businesses can unlock the full potential of autonomous AI agents, providing a foundation for scalable, interoperable, and secure AI-driven workflows.

What is an MCP server?

An MCP server is a backend service that implements the protocol, enabling real-time context synchronization between AI agents and the tools they interact with. Unlike traditional APIs, which process isolated requests, MCP servers share context across systems in real time, ensuring that agents operate on the most up-to-date information. They also manage fine-grained permissions, ensuring agents only access the data source they are authorized to interact with.

Real-World example: Collaborative document editing (e.g., Notion or Google Docs)

In a collaborative document editing scenario, such as in Notion or Google Docs, an AI agent can update the document context in real time. For instance, when an agent updates a section in a shared document (e.g., adding new content on security protocols), it instantly synchronizes that context across all connected collaborators. While traditional APIs would require isolated requests to update each user’s view of the document, an MCP server ensures that everyone sees the latest changes in real time, maintaining seamless collaboration.

Unlike traditional APIs, MCP servers continuously synchronize context across systems, keeping AI agents up-to-date in real time. APIs execute requests in isolation, while MCP servers enable event-driven updates using stateless, ephemeral calls, making the workflow more efficient and synchronized across platforms.

How MCP servers differ from traditional APIs

Unlike traditional APIs that handle isolated requests, MCP servers continuously synchronize context across systems, ensuring agents work with the most up-to-date data source. Key features like OAuth 2.0 support, scoped permissions, and stateless agent calls make MCP servers ideal for dynamic, autonomous workflows. In an AI system that uses LLMs, MCP protocol enables the smooth and secure integration of large-scale, real-time decision-making processes.

• OAuth 2.0 support with scopes tied to model context permissions: In a CRM application, an AI agent can use OAuth 2.0 with scoped permissions to access only the customer data relevant to a specific project, ensuring the agent doesn’t have unnecessary access to other sensitive data, like financial details or personal records.
• A structured schema that clearly defines available context objects, events, and actions: For example, in an e-commerce platform, the AI agent might retrieve context such as "order ID", "customer details", and "payment status" using a defined schema, ensuring it processes the exact data needed for the task at hand, like updating an order status or triggering a shipment notification.
• Stateless, ephemeral agent calls: In a supply chain management system, an AI agent might query the real-time status of shipments. Each request is stateless, meaning the agent only retrieves the most current status for that specific query without needing to store any session or previous data, making the process more efficient and scalable.

MCP servers as context brokers in AI workflows

MCP servers act as central context brokers, enabling AI systems to access and synchronize context across multiple data sources. This facilitates complex workflows, such as fraud detection in banking apps. For example, an AI agent in a banking app uses the MCP server to access transaction data, account information, and user behavior from multiple data sources, enabling real-time decision-making, such as flagging fraudulent transactions.

Real-world examples with payloads

Collaborative document editing (Notion/Google Docs)

• Use case: Agents can update document context in real-time, enabling seamless collaboration and ensuring version control.
  
• Payload example (Context update):

In this case, an MCP client synchronizes the updated document context (which includes the changes made, the user who made them, and the time of update) across all connected tools. This ensures that all collaborators have the latest context and no data source is out of sync.

AI-driven workflow orchestration (Zapier)

• Use case: AI agents trigger workflows based on new data, such as updating CRMs, sending emails, and generating reports.
  
• Payload example (Trigger workflow):

Here, an AI agent detects new customer data, triggers actions like sending a welcome email, and updating the CRM. The context of the customer data and actions is synchronized across systems, ensuring that all tasks are performed in real-time.

How to build an MCP server

Building an MCP server involves creating a backend service that manages the context of the AI model for agentic clients. The server should ensure real-time synchronization, data security, and scalability.

Expected structure and components

To build a robust MCP server, you'll need to define key components that ensure efficient context management:

• API schema: Define context endpoints and payloads.
• Authentication & identity: Implement OAuth 2.0 for secure access.
• Client registration: Use Dynamic Client Registration (DCR) for agent onboarding.
• Endpoint design: Optimize for real-time calls using REST or JSON-RPC.

Required standards

Building an MCP server requires setting up key components:

• OAuth 2.0: Use scoped permissions to restrict agent access to specific context elements, ensuring that agents can only act on data they are authorized to modify or access.
• PKCE (Proof Key for Code Exchange): Implement PKCE ^{code_challenge} for enhanced security, especially when dealing with public clients that cannot securely store secrets.
• OpenAPI or JSON schema: Use these standards to describe API contracts and context schemas clearly. This provides a clear, structured way for agents and systems to interact with the server and understand the context data.

Security best practices

When building an MCP server, security is a top priority. The following best practices ensure that your MCP server is secure and reliable:

• Secure token storage: Store tokens securely, ensuring they are never exposed or stored in an insecure way. Use encrypted storage for all sensitive data.
• Rate limiting: Implement rate limiting to avoid abuse from agents making too many calls in a short period.
• Audit logging: Track and log every access and modification of context data. This provides transparency and helps track down issues or monitor usage for compliance.
• Identity delegation and impersonation: Carefully handle identity delegation and impersonation requests to ensure that agents only act on behalf of users they are authorized to represent.

Performance & scalability considerations

To handle high-frequency, real-time context updates, your MCP server must be designed to scale efficiently:

• High-frequency, ephemeral calls: Ensure your server can handle high-frequency agent calls that are typical of autonomous workflows. This often involves stateless operations that don’t store session data but rely on each call being independent.
• Error handling and retries: Implement robust error handling and retry mechanisms to ensure reliability in the face of transient errors or network issues. Exponential backoff can be useful to avoid overwhelming systems during peak usage periods.

What MCP servers unlock

MCP servers unlock the full potential of autonomous AI workflows, including:

App interoperability

MCP servers allow seamless, real-time context sharing across different applications and AI agents, reducing integration complexity. By using a common context-sharing protocol, systems can easily exchange data and work together without the need for custom connectors or complex integration logic.

Agent-Initiated automation

MCP servers (multi-channel protocol) allow AI agents to initiate multi-part workflows based on a shared context autonomously. What that means is that to automate your business’s updating of CRM, emailing, and reporting that is done without human intervention, the agents are using the most recent data.

Scalable workflows

MCP’s design ensures workflows scale efficiently across platforms. Enabling context chaining supports real-time, high-frequency automation, making it easier to monitor and audit agent-driven actions. This scalability is essential for businesses needing seamless, automated processes with full visibility and compliance.

New use cases enabled

MCP servers unlock exciting new possibilities, such as:

• Agentic data aggregation: Collecting data from multiple sources for real-time decision-making.
• Decision-making pipelines: Automating business logic and decisions across systems based on shared context.
• Event-driven automations: Triggering workflows based on changes in context or actions taken by agents.
• AI-assisted business logic: AI assistants autonomously manage and optimize workflows to streamline operations.

Design considerations for MCP servers

Building an efficient MCP server requires addressing key design factors to ensure security, performance, and scalability. These considerations are critical to handle high-frequency agent calls, provide secure data access, and scale the system effectively.

Identity & authorization integration

MCP servers must implement fine-grained permission scopes to ensure that agents only access the context they’re authorized to interact with. For instance, agents in a banking app should only access specific account data based on predefined permissions. Delegated authorization and impersonation features must be carefully managed with proper audit trails to maintain transparency.

Rate limiting and throttling

With high-frequency agent calls, rate limiting is crucial to prevent system overload. For example, when a customer support agent queries multiple systems for a report, rate limits ensure that the server doesn't become overwhelmed. Defining limits per client, agent, and endpoint helps ensure fair resource allocation and prevents abuse, particularly in large-scale environments with many simultaneous requests.

Auditability and monitoring

To maintain compliance and security, every context update must be logged with detailed metadata. For instance, if an agent accesses sensitive customer data, that action should be logged for compliance checks. Integrating with SIEM systems or monitoring dashboards allows for proactive tracking and fast responses to potential issues.

Permission granularity and security

MCP servers must follow the principle of least privilege. This ensures that agents only interact with the necessary data. For example, a finance agent should only access transaction data and not personal user information. Additionally, securely managing token expiration and refresh cycles is critical to prevent token hijacking risks.

Failure handling and retry logic

MCP servers should have clear error messaging, helping agents understand and resolve issues quickly. Implementing exponential backoff and retry mechanisms ensures that transient failures do not overwhelm the system, improving overall system resilience. For example, if an agent fails to retrieve data from a third-party service, retries should be performed with backoff intervals to avoid further strain.

Examples and directories

While the Model Context Protocol (MCP) is emerging, several platforms are already implementing similar context-sharing principles, enabling more interoperable AI workflows. These platforms expose application states and workflows to AI agents through context-aware APIs, aligning with MCP’s goal of providing secure, synchronized context across systems.

• Notion: Offers APIs for intelligent document collaboration, allowing agents to query and update document context in real time and aligning with MCP’s real-time synchronization.
• Slack: Provides event-driven APIs that trigger context-rich workflows, facilitating AI interactions and automated actions based on shared context, showcasing MCP-like automation in messaging.
• Zapier: Automates workflows across apps using context sharing, enabling multi-app orchestration similar to MCP’s agent-driven automation.

These examples show how context-sharing and synchronization across platforms improve operational efficiency and automation, akin to MCP workflows.

MCP registries and directories

As MCP adoption grows, directories help developers discover and integrate MCP-enabled services:

• Claude MCP registry: A curated directory listing MCP-enabled services for AI agents, simplifying tool integration.
• OpenAgents directory: Offers metadata and discovery for platforms supporting MCP or similar protocols, making it easier to find and adopt MCP-enabled tools.

Community resources

As MCP adoption expands, community resources offer support:

• Official MCP specifications and documentation: Detailed resources covering MCP’s protocol and use cases.
• SDKs and libraries: Tools for implementing MCP servers and clients, simplifying integration.
• Developer forums: Communities discussing best practices, troubleshooting, and MCP features.
• Tutorials and example projects: Guides and sample projects demonstrating MCP server implementation.

Future outlook

MCP is expected to gain widespread adoption in AI ecosystems as businesses move toward autonomous systems. Key trends include:

• Standardization of context sharing: Cross-industry efforts to standardize context protocols will foster interoperability.
• Open-Source MCP tooling: Increased community contributions to MCP tooling will accelerate adoption.
• Broader adoption: Major platforms are expected to adopt MCP, driving the shift toward scalable, secure AI workflows.

Conclusion

MCP servers enable autonomous AI workflows by providing real-time, synchronized context and granular access control. They unlock new possibilities for agent-driven automation, scalability, and seamless interoperability, helping businesses build more intelligent, more efficient systems.

To stay ahead in the evolving world of AI-driven automation, developers and engineers must explore how to implement MCP servers and engage with the growing community and emerging standards around them. Start by learning how to secure your MCP implementations using best practices in authentication and authorization at Scalekit MCP auth. Embracing MCP now will help build more intelligent, scalable, and interoperable agentic applications in the future.

FAQ

1. How does MCP differ from traditional APIs?

Traditional APIs like REST and GraphQL are primarily designed for isolated request-response interactions, where data is exchanged between systems without maintaining continuous context. In contrast, Model Context Protocol (MCP) focuses on real-time context sharing, allowing multiple systems to work with synchronized, versioned data that AI agents can access and update dynamically. This enables autonomous, context-aware workflows that traditional APIs are not optimized for.

2. Why are MCP servers important for agentic apps?

MCP servers serve as the backbone of autonomous AI-powered workflows by allowing agents to share and update context across multiple applications securely. They are essential for real-time data synchronization and granular access control, enabling AI agents to coordinate complex tasks across disparate systems with minimal human intervention. Without MCP servers, AI agents would struggle to maintain context across workflows, leading to inefficiencies and potential security risks.

3. How do MCP servers support interoperability between different applications?

MCP servers act as context brokers, facilitating seamless communication between diverse applications by ensuring that they all have access to consistent, up-to-date context. This enables cross-platform automation where applications can share data and perform actions based on shared context, reducing the complexity of integrating multiple systems. The use of standardized protocols makes it easier to integrate various applications without needing custom connectors or complex logic.

4. How does Model Context Protocol enable agent-initiated automation?

MCP allows AI agents to autonomously trigger workflows based on context changes, data updates, or external events. By having access to real-time, synchronized context, agents can initiate multi-step processes without human input. For example, an agent could update a CRM, send an email, or generate a report based on changes in the shared context, automating repetitive tasks and improving efficiency.

5. What role does OAuth 2.0 play in MCP server security?

OAuth 2.0 is used in MCP servers to manage authentication and authorization securely. It ensures that only authorized agents can access certain pieces of context data by using scoped permissions. This provides fine-grained control over what each agent can and cannot do, ensuring data security and compliance with regulatory standards. PKCE (Proof Key for Code Exchange) enhances security, particularly for public clients, by preventing the interception of sensitive authentication data.

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