Breaking Down Model Context Protocol (MCP)

MCP’s Client-Server Architecture

MCP is built on a client-server model that structures how AI models retrieve and interact with external data sources.

• MCP clients are AI agents, applications, or any system that requests structured data.
• MCP servers act as intermediaries, fetching data from various APIs, databases, or enterprise systems and returning it in a consistent format.

Instead of AI models making direct API requests, MCP servers handle the complexity of authentication, data retrieval, and response normalization. This means AI agents no longer need to manage multiple API credentials, different request formats, or inconsistent response structures.

For example, if an AI model needs to pull information from multiple services like Google Drive, Slack, and a database, it does not query each API separately. It sends a single structured request to an MCP server, which processes the request, gathers data from the necessary sources, and returns a well-organized response.

MCP Request-Response Lifecycle

A typical MCP interaction follows a structured request-response cycle that eliminates redundant API calls and standardizes data retrieval.

1. The AI agent sends a structured request to the MCP server. Instead of crafting individual API requests, the agent defines what data it needs in a uniform format.

{
  "request_id": "xyz-987",
  "queries": [
    {
      "source": "github",
      "action": "get_recent_commits",
      "repo": "company/project"
    },
    {
      "source": "slack",
      "action": "fetch_unread_messages",
      "channel": "engineering"
    }
  ]
}

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2. The MCP server processes the request by validating authentication, checking permissions, and determining which external systems to query.

3. Queries are executed in parallel, meaning data from multiple services is retrieved at the same time rather than sequentially, reducing overall latency.

4. Responses from different sources are standardized into a structured format that AI models can easily process.

{
  "github": {
    "recent_commits": [
      {
        "author": "Alice",
        "message": "Refactored AI pipeline",
        "timestamp": "2024-03-12T10:15:00Z"
      }
    ]
  },
  "slack": {
    "unread_messages": [
      {
        "user": "Bob",
        "text": "Hey, can you review the PR?",
        "timestamp": "2024-03-12T09:45:00Z"
      }
    ]
  }
}

Unlike raw API responses that require manual parsing, MCP ensures that all retrieved data follows a predictable, structured format, making it easier for AI models to understand and utilize.

Query Execution and Response Aggregation

MCP is designed to optimize how AI models interact with external systems by introducing a structured execution process.

1. Request validation ensures that the AI model has the necessary permissions before any data is retrieved.
2. Query routing determines which external services need to be accessed.
3. Parallel execution retrieves data from multiple sources at the same time, reducing delays caused by sequential API requests.
4. Response aggregation consolidates structured data into a single response, eliminating the need for AI models to manually process multiple raw API outputs.

By reducing redundant requests, normalizing responses, and handling authentication centrally, MCP eliminates unnecessary API overhead and makes AI-driven automation more scalable.

Authentication Challenges

One of the biggest promises of MCP is to make AI agents less dependent on API-specific integrations. However, authentication (AuthN) remains a major challenge.

Today, API authentication is a fragmented process—some services use OAuth, others rely on API keys, and some require session-based authentication. This inconsistency makes onboarding new APIs time-consuming, and MCP currently does not have a built-in authentication framework to handle this complexity.

MCP still requires some external mechanism to authenticate API requests, which means AI agents using MCP must rely on additional solutions, such as Composio, to manage API credentials. Authentication is on the roadmap for MCP, but until it is fully implemented, developers will still need workarounds to handle authentication across multiple systems.

Unclear Identity Management

Another unresolved issue is identity management—who does an external system see when an AI agent makes a request through MCP?

For example, if an AI assistant queries Slack via MCP, should Slack recognize the request as coming from:

• The end user? (Meaning the AI is acting on behalf of a human.)
• The AI agent itself? (Which would require Slack to handle AI-based interactions separately.)
• A shared system account? (Which could introduce security and access control concerns.)

This issue is even more complicated in enterprise environments, where access control policies determine who can retrieve what data. Without clear identity mapping, MCP integrations could face restricted access, security risks, or inconsistencies across different platforms.

OAuth support is planned for MCP, which may help clarify identity handling, but until this is fully implemented, AI models may struggle with permission-based access to third-party services. 

Vendor Lock-in and Ecosystem Fragmentation

MCP is currently an Anthropic-led initiative, which raises questions about its long-term standardization. As AI ecosystems evolve, there is a strong possibility that other major players—such as OpenAI or DeepSeek—will develop their own protocols for AI-to-system interactions.

If multiple competing standards emerge, the industry could fragment, forcing developers to choose between different, incompatible approaches. Whether MCP remains the dominant approach or simply becomes one of several competing options remains to be seen.

1. Can MCP be configured to comply with SOC 2, HIPAA, or GDPR standards?

Yes, but it depends on how you implement and host it. Since MCP is just a protocol, you'll need to ensure your MCP server handles data securely, with encryption, access controls, and audit logging, to meet compliance requirements.

2. How do AI agents determine when to trigger MCP versus relying on internal memory?

Typically, they rely on prompt logic or custom rules, like checking if the info might be outdated or unavailable internally. If a question requires fresh or external data, that’s when the AI agent calls on MCP to fetch it.

3. Is MCP compatible with existing RAG (retrieval-augmented generation) architectures?

Yes. MCP can plug right into a RAG setup by acting as the structured data retriever, replacing manual API calls with a more consistent and streamlined approach.

4. What types of business workflows benefit the most from MCP integration?

Anything involving lots of tools or data silos, like customer support, internal IT ops, sales enablement, or knowledge management, can benefit big time from MCP simplifying how AI agents get the info they need.

5. How can startups adopt MCP without overhauling their entire data architecture?

Start small. You can use MCP for just one or two key integrations first, like Slack or a CRM, then expand as needed. Since it abstracts away most of the messy API work, it’s a pretty lightweight lift to begin with.