Model Context Protocol: The Interface Layer for Intelligent Agents

Over the past year, Large Language Models (LLMs) have evolved from a novelty to a central component of modern software. But for all their intelligence, LLMs remain constrained by one thing: context.

While they excel at understanding and generating text, LLMs lack built-in mechanisms to interact with external systems, reason across multiple steps, or retain information over time. To address this, many teams (ours included) turned to Retrieval-Augmented Generation (RAG) — injecting documents and metadata into prompts. RAG helps, but it's not enough: it treats LLMs as passive responders, not active participants.

Enter the Model Context Protocol (MCP), a specification from Anthropic released at the end of 2024. MCP is an interface paradigm that allows LLMs to incorporate external context, enable tool calls, and interact with systems, going beyond just reading and generating text. This allows LLMs to act, plan, and adapt over time—traits you’d expect from autonomous systems.

From Language Models to Autonomous Agents

LLMs were originally designed as stateless completion engines. You give them a prompt, they give you a reply. However, as developers integrated LLMs into workflows and applications, several limitations became apparent:

• They forget everything outside the current prompt.
• They can’t take real-world actions.
• They can’t handle multi-step processes.
• They’re difficult to supervise and debug.

To address these limitations, Retrieval-Augmented Generation (RAG) was introduced. RAG adds more context beyond what models know from training and fine-tuning by injecting documents and metadata into prompts. This enhancement helps, but it still treats LLMs as passive responders rather than active participants.

The next significant advancement was function calling, which allowed LLMs to emit structured outputs, such as JSON, that could trigger backend functions. This innovation transformed LLMs from mere responders into planners capable of taking action. However, this also raised new challenges:

• How do you define which tools are available to the model?
• How do you pass in relevant memory or context?
• How do you track the interaction across many steps?
• How do you ensure transparency and traceability?

The Model Context Protocol (MCP) addresses all these challenges. It standardizes the process of integrating external context and tools, turning LLMs into cooperative, auditable, tool-using agents. MCP provides a structured framework that enables LLMs to think, remember, act, plan, and adapt over time, effectively bridging the gap between stateless LLMs and autonomous agents.

What Is MCP?

The Model Context Protocol (MCP), developed by Anthropic, is a new standard that defines how LLMs can interact with the world around them. It introduces a consistent, structured way for models to operate more like intelligent agents — not just passive responders.

With MCP, LLMs can:

• Engage in multi-turn conversations with memory across steps.
• Call tools and APIs using structured, machine-readable inputs.
• Access external context (like metadata, logs, or documents) in a clean, modular way.
• Maintain a clear separation between what the model decides and how the system executes those decisions.

You’ll often hear MCP compared to REST — and while they’re not technically equivalent, the analogy helps set the stage:

• REST standardized how developers connect services over the web.
• MCP is emerging as a standard for how LLMs interface with tools, memory, and real-world systems.

MCP is not built on HTTP or REST principles; it is not a direct parallel. But conceptually, it plays a similar role: a common protocol that helps modular, scalable, and interoperable systems emerge. If REST defined the interface layer for the web, MCP is defining the interface layer for intelligent, language-native agents.

MCP organizes interactions around four key building blocks:

1. Messages – These capture the back-and-forth conversation, with roles like user, assistant, tool_use, and tool_result. They make the dialogue easy to track and manage.
2. Resources – External information (like pipeline configs or logs) that the model can access, without cramming it all into the user’s prompt.
3. Tool Use – The model can call tools with structured input, get results back, and keep going — similar to how a person might use a search engine or database mid-task.
4. Prompts – In MCP, a prompt includes all of the above: messages, tools, resources, and context. It’s not just one piece of text — it’s the whole session the model sees and builds on.

Challenges and Limitations

While the Model Context Protocol (MCP) offers a standardized framework for integrating LLMs with external tools and data sources, its implementation presents several challenges:

• Security Vulnerabilities

MCP's design introduces potential security risks. Recent studies have highlighted vulnerabilities such as unauthorized tool usage, remote code execution, and credential exposure. These risks stem from the protocol's ability to allow LLMs to invoke external tools, which, if not properly secured, can be exploited by malicious actors.

• Engineering Complexity

Developing and maintaining MCP integrations require significant engineering effort. This includes designing tool interfaces, managing session states, and ensuring seamless communication between components. The complexity increases with the number of tools and data sources involved, potentially leading to scalability issues.

• Ecosystem Maturity

As a relatively new protocol, MCP's ecosystem is still evolving. While early adopters have begun integrating MCP into their systems, widespread support and standardized practices are still developing. This can lead to inconsistencies and a lack of best practices for implementation and security.

Why We’re Excited About MCP at Sifflet

At Sifflet, we’re building AI that helps teams understand, monitor, and resolve issues in their data systems.

MCP enables us to:

• Expose diagnostic and remediation tools via tool calls.
• Inject pipeline metadata, user state, and logs as structured resources.
• Maintain memory and context across multi-turn incident investigations.
• Build agents that don’t just answer — they observe, decide, and act.

We’re actively building our MCP-based agent layer, and it’s launching soon. Stay tuned.

Final Thoughts

MCP isn’t just a spec — it’s a shift in how we think about LLMs. It turns them from reactive text predictors into interactive, memory-aware agents that can truly operate in complex systems.

It enables applications where:

• Agents plan and execute over long sessions.
• Tools are modular, auditable, and composable.
• Context is dynamic, structured, and injected from outside.

MCP represents a significant leap forward in the evolution of LLMs. By enabling context-aware, interactive, and tool-using agents, MCP paves the way for more intelligent and autonomous systems. While challenges remain, the potential benefits are immense.

At Sifflet, we are excited to be at the forefront of this revolution. Stay tuned for our upcoming launch and visit our resources page to learn more about how we are empowering agents to interact with Sifflet