Beyond Prompts: How MCP Powers the Future of AI Agents
- Nischay Bagusetty
- May 3
- 7 min read
Updated: May 21
Introduction
The term that has dominated the Generative AI conversation this year is “AI Agents”, also called "Agentic AI" . From hackathons to product roadmaps, agent-based systems have become the hottest trend in the AI world. They come with a promise of smarter automation, personalised workflows, collaborative intelligence and much more. But what exactly is an AI agent?
Broadly speaking, an AI agent is a system that can understand its environment, reason about it, and act autonomously to achieve a defined goal. Unlike traditional software scripts, AI agents are adaptive and are autonomously capable of making decisions based on dynamic input, evolving tasks, and feedback from the environment.
The Emerging Problem in Agentic AI
As AI Agents continue to improve, ecosystems where multiple agents work together and communicate with each other began to develop in order to tackle complicated, multi-step tasks. They work like a team of human collaborators where they solve complex problems by distributing tasks among themselves. But this evolution brings with it a core challenge: how do these agents stay on the same page?
Large Language Models (LLMs), the foundation of many AI Agents today are stateless by design. They don’t “remember” previous conversations unless we manually pass that context each time. This approach works in simple workflows, but in multi-agent systems, it leads to breakdowns in coordination, duplication of effort, and loss of long-term understanding.
This is where the Model Context Protocol (MCP) enters the picture. Developed by a company called Anthropic and officially announced in November 2024, MCP proposes a standard way for agents to initialise, share, and evolve context throughout a task. It is widely recognised and adopted by major AI providers like Open AI, Microsoft (As part of their SDKs), Google (Google announced MCP support as part of their A2A Protocol)
Let’s unpack how MCP could become the backbone of multi-agent AI ecosystems. In this blog, we explore:
What is MCP.
How MCP works under the hood — the lifecycle of context
Real-world use cases and applications.
Challenges in implementation and best practices.
What is MCP?
Model Context Protocol is an open standard/specification designed for facilitating seamless integration between AI Models, tools and external data sources. As we earlier discussed, LLMs are stateless and can lead to hurdles in Multi-Agentic frameworks. MCP addresses this by offering a formal structure for memory exchange, tool interaction, and environment state, enabling persistent task progress and agent coordination. MCP essentially gives agents access to shared context objects and tool APIs via a centralised MCP server. The overall MCP framework includes:
An MCP Server: This component exposes specific tools, APIs, data sources, or other capabilities. It understands and responds to requests formatted according to the MCP protocol. Think of it as the provider of a specific function or piece of information.
An MCP Client: This is the component responsible for communicating directly with an MCP Server using the standardised MCP protocol. It sends requests to the Server (e.g., to execute a tool function or retrieve data) and receives responses. Clients are typically used by an MCP Host.
An MCP Host: This is the AI application or the AI Agent itself. The Host coordinates the overall task, manages security and lifecycle policies, and integrates the AI/LLM's reasoning capabilities. It decides when to use external tools or data and instructs the appropriate MCP Client to interact with the corresponding MCP Server. A Host can manage multiple Client instances.
A Context Object: This is a specifically structured object which forms the medium for memory exchange. It carries information pertaining to the evolving context of the complex task, allowing for synchronisation across different agents (Hosts) or different steps taken by the same agent (Host).
MCP essentially gives agents (acting as Hosts) access to shared context objects and allows them to leverage tool capabilities via the standardised Client-Server communication pathway. It offers a shared language and a plug-and-play interface that removes much of the friction in integrating and coordinating AI Agents.
Inside the MCP Lifecycle: How Context Actually Moves
We have seen the 3 main components that are part of MCP. The real magic lies in how these parts communicate, evolve, and stay in sync to maintain continuity across agent actions. MCP isn’t just a wrapper around LLM prompts; it formalises a turn-by-turn protocol for coordination, allowing agents to operate over time, maintain memory, and collaborate with tools or other agents. Lets observe how this happens during a typical work cycle:
A Task Begins:
The cycle begins when the AI Agent (acting as the MCP Host) receives a task. The Host consults the Context Object, looking for any existing information, such as past steps, goals, or constraints relevant to the current task.
Context Is Queried and Interpreted:
Before taking significant action, the AI Agent(Host) reads the Context Object. This gives the Host/Agent continuity and the necessary state information. The Host (often leveraging its underlying LLM) uses this context to reason and determine the next required action(s).
Tool or Model Calls via MCP Server:
If the Host decides an external tool or data source is needed, it instructs the appropriate MCP Client to interact with the designated MCP Server. The Client sends a request, formatted according to the MCP standard, to the Server. The Server executes the request (e.g., runs the tool, fetches data) and sends the result back to the Client. The Client then relays this result back to the Host. The MCP Server can act as a secure gateway, potentially shielding sensitive systems from direct interaction with the Host/LLM.
Context Is Updated :
After receiving the result from the Client (or completing an internal reasoning step), the Host updates its Internal Dynamic Context. This update typically includes information like:
The tool/server that was called (if any).
The output received from the tool/server.
The Host's/Agent's reasoning for the action taken.
Any changes to the overall task state. This write operation ensures the shared memory reflects the latest progress.
Turn Continues or Handoff Occurs :
The updated context is now available for the next round of reasoning. This might be the same Host/Agent continuing the task or, in a multi-agent system, another Host/Agent picking up the task based on the latest context. This turn-by-turn orchestration continues until the task is complete or handed off.
Advantages - Why this matters
In conventional LLM apps, long tasks often break due to prompt size limits or lack of memory. MCP sidesteps this by:
Externalising memory — storing structured context outside the model.
Decoupling logic — letting agents reason based on current context, not raw prompts.
Modularising behaviour — enabling tool-use, delegation, and planning via a clean interface.
Trust Through Mediation – Routing data and tooling through MCP servers can help enable access policies, rate limits, and audit logs — making it safer for enterprise and regulated environments.
This makes MCP a strong foundation for frameworks like AutoGen, Google’s ADK, or any custom multi-agent stack where reliability, persistence, and scalability are key.
Real-World Use Cases and Applications
The potential of MCP lies in its ability to orchestrate complex tasks that were previously difficult or impossible to automate reliably with standalone LLMs or simpler integrations. As such, applications for MCP can be found in Agentic systems that are built to handle complex tasks like:
Sophisticated Customer Support Automation
Customer support systems tackling complicated issues that span across different areas like billing problems, technical troubleshooting and other domains can leverage MCP. Different MCP servers built to tackle different issues can be created to provide agents with specialised tools. A central MCP server can also co-ordinate between multiple MCP servers so that context is not only maintained for different staged processes inside a server but also across multiple servers.
Complex Data Analysis and Reporting
A research task might involve one agent gathering data from diverse sources (web scraping, databases), another agent cleaning and structuring the data, a third performing statistical analysis using specialized tools (called via the MCP Server), and a final agent synthesizing the findings into a report. MCP maintains the state of the research—data gathered, analysis parameters, intermediate results—allowing each agent to build upon the previous work accurately.
Autonomous Software Development & DevOps
An MCP-powered system could monitor application logs (Agent 1), identify potential bugs, attempt automated fixes using coding tools (Agent 2), run tests in a staging environment (Agent 3 accessed via MCP Server), and if successful, deploy the fix and update documentation (Agent 4). The Context Object tracks the bug status, attempted solutions, test results, and deployment state throughout the lifecycle.
Streamlined Business Process Automation
Consider an order fulfilment process. An agent verifies inventory (using an API via MCP Server), another processes the payment (secure tool call), a third coordinates shipping logistics, and a fourth sends notifications. MCP acts as the central nervous system, ensuring the order status and relevant details are consistently updated and accessible to the right agent at the right time.
Challenges in MCP Adoption and Implementation
Employing MCP for Agentic frameworks comes with its own set of challenges. Ranging from maturity of MCP and its adoption to performance and scalability, MCP adoption needs careful thought in design and implementation. More on the challenges below:
Maturity and Standardisation
As a relatively new standard, interpretation and implementation may vary between providers in the early phases. This can prove to be a challenge especially when trying to integrate with MCP servers built by different third party providers. With passing time however, the community will naturally evolve into a standard interpretation that can increase interoperability
Context Object Design
As the basis of information storage and exchange, the design of the context object is very crucial and challenging. It needs to be comprehensive enough to ensure continuity in the context but light enough to be manageable and performant. Overly complicated or extremely limited designs can potentially reduce the benefit of adopting MCP.
Error Handling and Resilience
In a multi-agent system, failures can occur at multiple points (agent logic, tool execution, MCP server availability, context corruption). Robust error handling, state recovery mechanisms, and transaction management are essential for reliability. Debugging and Observability also need to be handled properly given the complicated flow of information across different tools and agents.
Security and Access Control
The MCP Server and Context Objects can become central hubs for sensitive information and powerful tool access. As such, implementing fine-grained access control to ensure agents only read/write relevant context portions, use specific tools is a challenge.
Performance and Scalability
The MCP server can become a bottleneck if not designed to handle numerous concurrent context reads/writes and tool requests from many agents. Latency in accessing context can slow down the entire system.
Conclusion
The rise of AI agents marks a shift towards more autonomous, capable, and collaborative AI systems. However, the inherent statelessness of LLMs has been a significant barrier to realising the full potential of multi-agent frameworks.
The Model Context Protocol (MCP) offers a compelling solution – a standardised way for agents to share understanding, coordinate actions, and maintain state throughout complex tasks. While implementation challenges remain, the growing adoption by major AI players signals its importance. MCP, or standards like it, are positioned to become important for the next generation of AI applications, enabling intricate workflows and collaborative intelligence that were previously out of reach. It represents a crucial step towards building AI systems that can not only reason but also remember, coordinate, and persist in achieving complex goals.
