Model Context Protocol (MCP): Engineering Context for LLMs
- Nagesh Singh Chauhan
- 3 minutes ago
- 11 min read
The future of AI is not defined by larger models, but by better context and orchestration.
Introduction
Large Language Models (LLMs) have rapidly evolved from standalone text generators into reasoning engines capable of interacting with tools, APIs, databases, and live systems. However, as these systems grow more complex, a fundamental challenge emerges:
How do we reliably supply the right context, at the right time, in a structured and auditable way?
The Model Context Protocol (MCP) addresses this challenge by standardizing how context, tools, memory, and environment state are exposed to language models. MCP acts as a control plane for AI cognition, ensuring predictable, composable, and scalable interactions between LLMs and the external world.
This article provides a deep technical exploration of MCP—covering architecture, components, data flow, design patterns, and real-world use cases.
Problems MCP Solves: Why It Became Necessary
Before Model Context Protocol (MCP), integrating AI models with real-world systems was messy and inefficient.
LLMs had two core limitations:
Context limitation – Models could only reason over what was manually injected into the prompt.
Inability to act – Models could generate text but couldn’t reliably interact with external tools or data.
This led to the M×N integration problem:
Connecting M AI models to N external tools required M×N custom integrations.
Example: 4 models (Claude, GPT-4, Gemini, DeepSeek) × 5 tools (GitHub, Slack, Google Drive, Salesforce, Internal DB)→ 20 custom integrations
MCP changes this to M + N:
One MCP client per model
One MCP server per tool
Result:
4 + 5 = 9 components→ ~55% reduction in integration complexity and development effort
What Is Model Context Protocol?
Model Context Protocol (MCP) is an open standard—introduced by Anthropic in late 2024—that establishes a universal interface for connecting AI models to external data sources, tools, and execution environments.
A helpful way to think about MCP is as a USB-C port for AI systems. Just as USB-C standardizes how phones, laptops, and cameras connect to chargers, displays, and storage devices, MCP standardizes how AI models interact with data, APIs, and tools—regardless of the underlying model or provider.
Prior to MCP, integrating AI with real-world systems was like traveling with a bag full of incompatible chargers: every new data source or tool required custom glue code, bespoke prompts, and fragile integrations. MCP replaces this complexity with a plug-and-play abstraction layer, enabling seamless, reusable, and model-agnostic connections between AI systems and the external world.
Overview of MCP Architecture
At its core, the Model Context Protocol (MCP) follows a clean, modular architecture that will feel very familiar to anyone who understands basic web or client–server systems. MCP deliberately avoids complexity by separating responsibilities clearly between components.
1. MCP Server
An MCP Server is a lightweight service that exposes a specific capability or data source using the MCP standard.
Each server usually connects to one system or service
Examples include:
Local file systems
Databases
Slack, GitHub, or other SaaS tools
The server understands how to fetch, update, or execute actions on that system
You can think of an MCP server as an adapter: it translates MCP requests into real-world operations and translates the results back into a format the AI can understand.
2. MCP Client
The MCP Client lives inside the AI application and manages communication with one or more MCP servers.
Maintains persistent or session-based connections
Sends structured MCP requests
Receives structured responses
In most cases, developers never interact with the client directly. It is embedded into AI platforms, SDKs, or agent frameworks and works transparently in the background.
3. MCP Host (AI Application)
The MCP Host is the AI-powered application that wants to use external data or tools.
This could be:
A chat assistant
An IDE AI helper
A background agent
A workflow automation system
The host uses an LLM to reason about what it needs, while MCP defines how that need is fulfilled.
4. Data Sources & Services
These are the actual systems where information or functionality resides:
Local: files, folders, local databases
Remote: APIs, cloud services, enterprise tools
MCP does not replace these systems—it simply provides a standardized bridge to them.
Putting It All Together
The interaction model is intentionally simple:
The AI host decides it needs external data or an action
The MCP client sends a structured request
The MCP server performs the operation on the underlying data source or tool
The result is returned in a structured response
The AI continues reasoning with fresh, grounded context
Conceptually:
AI (Host) → MCP Client → MCP Server → Data / Tool → MCP Server → MCP Client → AI
For example:
“Give me report.pdf”
“Run this database query”
“Fetch recent Slack messages”
All of this happens using MCP’s standardized language, without custom prompts or brittle glue code.
Why This Architecture Matters
This separation delivers major benefits:
Modularity – Each data source is isolated behind its own server
Reusability – The same MCP server works across multiple AI apps
Scalability – New tools are added without changing the model
Reliability – Clear contracts replace prompt-based guesswork
In short, MCP turns AI integrations from fragile, one-off hacks into a clean, scalable system architecture—much like how REST standardized web services or USB standardized hardware connectivity.
MCP Core Concepts
At the heart of the Model Context Protocol (MCP) is a small, well-defined set of interaction types that describe how an AI model can engage with external servers. These concepts intentionally mirror familiar software patterns, making MCP both powerful and intuitive.
MCP doesn’t allow arbitrary interaction. Instead, it constrains AI behavior into clear, auditable primitives—each with a distinct purpose.
1. Resources — Fetching Information
Resources represent data or content that an MCP server can provide to the AI.
Conceptually similar to a GET endpoint in web APIs
Used when the AI needs to read or load information
No side effects—purely informational
Examples:
Reading a file: file://README.md
Fetching a configuration document
Loading reference data from a database
Resources ensure that information access is explicit, structured, and controlled, rather than being implicitly embedded into prompts.
2. Tools — Taking Action
Tools allow the AI to perform actions through an MCP server.
Comparable to POST or PUT endpoints
The AI supplies structured input
The server executes code or triggers side effects
Examples:
Running a calculation
Updating a database record
Sending a Slack message
Creating a file or ticket
Tools transform the AI from a passive reasoner into an active operator, while still keeping all actions transparent and governed.
3. Prompts — Reusable Intelligence
Prompts are reusable prompt templates or workflows provided by the server.
Think of them as predefined reasoning recipes
Useful for complex or repetitive tasks
Encapsulate best practices and domain expertise
Instead of rewriting long instructions each time, the AI can request a prompt from the server and apply it consistently. This improves standardization, reliability, and maintainability across AI workflows.
4. Sampling — Two-Way Intelligence
Sampling is a more advanced capability that enables bidirectional interaction between the AI and the server.
The server can ask the AI to:
Complete text
Summarize content
Classify or transform data
Enables collaborative workflows where:
The AI fetches data from the server
The server asks the AI to analyze or enrich that data
This turns MCP into a conversation, not just a command interface—allowing richer, more dynamic agent behavior.
A Simple Kitchen Analogy
Imagine an AI chef working in a kitchen:
Resources are ingredients taken from the pantry (data the AI can read)
Tools are kitchen appliances the chef can operate (actions the AI can perform)
Prompts are recipes the chef can follow (structured guidance for tasks)
Sampling is like a sous-chef asking the head chef to taste and adjust (two-way collaboration)
Each element has a clear role, and together they enable sophisticated outcomes without chaos.
How MCP Communicates?
The Model Context Protocol (MCP) is designed with two core goals in mind: secure interaction and flexible deployment. Because MCP servers can expose sensitive data or trigger powerful actions, communication is intentionally explicit, structured, and controlled.
At a high level, MCP defines what is communicated and how it is communicated—while remaining agnostic to where the server runs.
Security First by Design
Since MCP servers may:
Access private files
Query databases
Call internal APIs
Perform write or execution actions
the protocol places strong emphasis on security boundaries.
Key security principles include:
Access controls at the server level – servers decide what they expose and to whom
Explicit tool invocation – models cannot silently act; every action is structured and auditable
Human-in-the-loop approval – AI hosts often require user consent before executing sensitive tools
This ensures MCP integrations are powerful, yet predictable and safe.
Communication Transports in MCP
MCP supports multiple communication “transports.” A transport simply defines how messages move between the AI host and the MCP server. Importantly, the protocol remains the same regardless of transport.
1. STDIO Transport (Local Mode)
In STDIO mode:
The MCP server runs as a local process
Communication happens via standard input and output streams
Why it’s useful:
Ideal for local development
Very simple to set up
Naturally secure (no network exposure)
Low latency
This mode feels similar to how CLI tools interact with other programs and is often the default for local or desktop-based AI applications.
2. SSE / HTTP Transport (Remote Mode)
In SSE (Server-Sent Events) or HTTP mode:
The MCP server runs as a web service
The AI host communicates via HTTP endpoints
Why it’s useful:
Enables remote or cloud-hosted servers
Supports distributed architectures
Easier to scale and share across teams or applications
This mode allows MCP servers to live anywhere—on another machine, inside a VPC, or in the cloud—without changing how the AI interacts with them.
Same Protocol, Different Pipes
Although STDIO and HTTP look very different operationally, they serve the same purpose:
They only differ in how bytes travel—not in what those bytes mean.
Under the hood:
MCP messages are structured
Requests and responses are encoded using formats like JSON
The schema remains consistent across transports
As a result, AI hosts don’t need to care where a server runs—only what it can do.
MCP versus RAG
Both Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG) improve LLMs with outside information, but they do this through different ways and serve distinct purposes. RAG finds and uses information for creating text, while MCP is a wider system for interaction and action.
Feature | Model Context Protocol (MCP) | Retrieval-Augmented Generation (RAG) |
Primary goal | Standardize two-way communication for LLMs to access and interact with external tools, data sources, and services to perform actions alongside information retrieval. | Enhance LLM responses by retrieving relevant information from an authoritative knowledge base before generating a response. |
Mechanism | Defines a standardized protocol for LLM applications to invoke external functions or request structured data from specialized servers, enabling actions and dynamic context integration. | Incorporates an information retrieval component that uses a user's query to pull information from a knowledge base or data source. This retrieved information then augments the LLM's prompt. |
Output type | Enables LLMs to generate structured calls for tools, receive results, and then generate human-readable text based on those results and actions. Can also involve real-time data and functions. | LLMs generate responses based on their training data augmented by text relevant to the query from external documents. Often focuses on factual accuracy. |
Interaction | Designed for active interaction and execution of tasks in external systems, providing a "grammar" for LLMs to "use" external capabilities. | Primarily for passive retrieval of information to inform text generation; not typically for executing actions within external systems. |
Standardization | An open standard for how AI applications provide context to LLMs, standardizing integration and reducing the need for custom APIs. | A technique or framework for improving LLMs, but not a universal protocol for tool interaction across different vendors or systems. |
Use cases | AI agents performing tasks (for example, booking flights, updating CRM, running code), fetching real-time data, advanced integrations. | Question-answering systems, chatbots providing up-to-date factual information, summarizing documents, reducing hallucinations in text generation. |
Cracks in the Architecture: Scaling Challenges for Remote MCP
The move toward remote-first MCP servers is inevitable—and strategically necessary. Remote servers unlock reuse, cross-application composability, and shared intelligence. But this shift also exposes fault lines in the current architecture. As MCP evolves from local adapters into distributed infrastructure, several non-trivial challenges emerge that directly impact security, reliability, and developer experience.
What works elegantly on a single machine becomes far more complex at scale.
1. Authentication & Authorization: A Blurred Security Model
The MCP specification points to OAuth 2.1 as the foundation for securing remote servers, but in practice this introduces significant complexity.
MCP servers are expected to act as both authorization servers and resource servers—a role combination that departs from standard IAM patterns. This blurring of responsibilities increases operational risk and makes correct configuration harder, especially for smaller teams.
The challenge compounds when:
Tools are chained across multiple MCP servers
Each server enforces different access policies
Identity must propagate across nested agent workflows
Unlike traditional APIs, MCP introduces agent-mediated identity, where access decisions are influenced not just by users, but by models acting on their behalf. This is a new security surface—and one that lacks mature best practices.
2. Security Risks: Tool Poisoning Attacks
A more subtle—and potentially dangerous—risk emerges from how MCP tools are described.
Recent research has highlighted Tool Poisoning Attacks (TPAs), where malicious instructions are embedded inside tool metadata or descriptions. Since LLMs treat these descriptions as trusted natural language context, a poisoned tool can:
Manipulate agent reasoning
Exfiltrate sensitive data
Trigger unintended actions
Corrupt downstream decision logic
The risk increases sharply when:
MCP servers are publicly accessible
Tool registries span organizational boundaries
No strong trust or verification layer exists
In a remote MCP ecosystem, context itself becomes an attack vector.
3. Fragile Infrastructure: Availability, Load & Failure Cascades
Local MCP failures are contained. Remote MCP failures are not.
When MCP servers become shared dependencies, high availability shifts from “nice to have” to mandatory. Agentic workflows often rely on chained tools, meaning a single upstream server outage can stall or collapse an entire execution plan.
Today, MCP provides:
No native load balancing
No failover semantics
No health signaling or redundancy model
As MCP usage grows, these gaps turn infrastructure hiccups into system-wide failures, especially in long-running or multi-agent workflows.
4. Developer Onboarding & Ecosystem Fragmentation
As the number of MCP servers increases, discoverability becomes a first-order problem.
Developers face unanswered questions:
Where do I find trusted MCP servers?
Which ones are actively maintained?
What tools do they expose, and how should they be used?
While registry concepts have been mentioned in roadmaps, today’s reality is a fragmented ecosystem with inconsistent documentation, unclear governance, and uneven quality.
Without strong discovery, versioning, and trust mechanisms, MCP risks repeating the early chaos of public API ecosystems—where reuse suffers despite good intentions.
5. Context Bloat & LLM Bias
Remote composability sounds clean—until you look at the context window.
Every remote MCP server adds:
Tool descriptions
Parameter schemas
Prompt templates
Capability metadata
In complex sessions, this leads to context bloat, driving up token usage, latency, and cost.
Worse, LLMs exhibit tool availability bias:
If a tool appears in context, the model is more likely to use it
Even when it’s unnecessary or inefficient
As more remote servers are registered, this bias can cause:
Redundant tool calls
Over-orchestration
Bloated execution chains
What begins as modularity can quietly degrade into inefficiency.
The Bigger Picture
Remote MCP servers are not a mistake—they are the future. But scaling MCP requires treating it less like a convenience layer and more like critical infrastructure.
To mature, the ecosystem will need:
Stronger trust and verification models
Clear identity and authorization boundaries
Built-in resilience patterns
Smarter context management and tool selection
MCP has standardized how AI connects to the world. The next challenge is ensuring those connections remain secure, resilient, and intelligible as the system scales beyond a single machine—and beyond a single team.
The Future of AI with MCP
The future of AI will be defined not by larger models, but by better system design—and Model Context Protocol (MCP) is at the core of that shift.
MCP transforms AI from isolated language models into context-aware, action-capable systems by standardizing how models access context, memory, tools, and policies. Instead of brittle prompt engineering and one-off integrations, MCP enables modular, reusable, and model-agnostic architectures.
As AI systems become more agentic, MCP will function as the operating system for intelligence—orchestrating reasoning, action, and learning across multiple models and tools. It enables auditability, governance, and safe autonomy, making large-scale AI adoption viable in enterprises and regulated industries.
In short, MCP moves AI from text generation to reliable cognition. The future of AI isn’t just smarter models—it’s structured intelligence powered by MCP.
Conclusion
Model Context Protocol represents a quiet but decisive shift in how artificial intelligence is built. It acknowledges a simple truth we surfaced throughout this discussion: powerful models alone do not create powerful systems. Without structure, context, and control, intelligence remains fragile.
MCP resolves the core fractures of modern AI—context limitation, inability to act, and integration sprawl—by introducing a clean, universal contract between models and the world they operate in. It replaces prompt improvisation with architectural clarity, collapses the M×N integration problem into a scalable M+N reality, and turns language models into dependable, context-aware agents.
More importantly, MCP reframes the future of AI. Intelligence is no longer something we merely invoke; it is something we engineer. With MCP, context becomes deliberate, actions become governed, and systems become auditable by design.
As AI systems grow more autonomous and embedded in critical workflows, the winners will not be those with the largest models—but those with the strongest foundations. MCP is that foundation: the protocol that allows intelligence to scale without losing reliability, safety, or trust.
In the end, the future of AI will not be written in prompts.It will be written in protocols—and MCP is the one that makes intelligence sustainable.