Model Context Protocol (MCP) - A Deep Dive

Introduction

Large Language Models (LLMs) have transformed how we interact with machines — from writing code and answering questions to automating workflows. However, despite their growing capabilities, LLMs are inherently limited by the static nature of their context. Once a prompt is passed, the model has no awareness of external tools, no ability to dynamically fetch fresh information, and no persistent memory of past tasks.

As AI systems evolve into autonomous agents capable of taking actions, making decisions, and collaborating across tasks, there's a growing need for models to communicate with external tools, services, and memory in a structured and reliable way. Existing ad hoc approaches like function calling or plugins only partially solve this — they lack consistency, security, and interoperability across ecosystems.

The Model Context Protocol (MCP) addresses this challenge by providing a standardized, model-agnostic communication layer between LLMs and external systems. Originally introduced by Anthropic and now being explored across the AI tooling ecosystem, MCP enables two-way, structured interactions between models and tools. It lays the foundation for composable, interoperable, and safe AI agents that can reason, act, and improve over time.

In this blog, we’ll explore MCP in technical depth — from its core architecture and interaction flow to its real-world applications, implementation challenges, and how it shapes the future of autonomous AI agents.

What is MCP ?

The Model Context Protocol (MCP) is an open, standardized protocol that defines how AI models can interact with external tools, data sources, and memory systems in a structured, controlled, and context-aware manner. Instead of treating models as isolated entities that only respond to text prompts, MCP introduces a systematic way for models to call tools, read or write context, and maintain persistent state across multi-step workflows.

At its core, MCP serves as an abstraction layer between the model and the external world. It acts as a bridge that translates the model’s intent into actionable operations, executes those operations in external systems (via tools defined by OpenAPI specifications), and returns the result back to the model in a predictable schema. This enables a feedback loop where models can request information, act upon it, and continue reasoning — all within a turn-based interaction cycle.

Unlike traditional prompt engineering or plugin systems that inject tool instructions as raw text, MCP interactions are strictly typed, validated, and managed by a runtime environment. This runtime ensures that the model’s actions are secure, auditable, and logically coherent.

MCP also supports a wide range of agentic capabilities, such as:

• Invoking external APIs through structured tool calls
• Maintaining a working memory or scratchpad across interactions
• Updating context over time
• Generating multi-step plans and acting on them incrementally

This design makes MCP model-agnostic and compatible with any LLM that understands the protocol schema — including Claude, GPT-like models, and open-source LLMs. As a result, it opens the door to building reusable tools and workflows that can work across models and use cases.

In essence, MCP transforms static model prompts into dynamic, multi-turn conversations with tools and memory — enabling a new class of composable, autonomous AI systems.

Core Architecture and Working Principles of MCP

The architecture of MCP is designed around a modular and model-agnostic structure that separates the responsibilities of the model, the tools it uses, and the protocol runtime that manages the entire lifecycle of interaction. This separation ensures flexibility, composability, and tight control over tool execution.

Core Architecture of MCP

The architecture includes five main layers:

1. Model

The model is the central reasoning agent. It consumes observations, generates responses, and decides when to invoke tools or update memory. Crucially, the model does not directly execute tools or access external systems. Instead, it emits structured messages—such as tool_use, context_update, or memory_write—which are interpreted by the MCP runtime.

The model interacts in a turn-based loop, producing actions (or thoughts) based on current context, and then waiting for the result before proceeding.

2. MCP Runtime

The runtime is the orchestration layer that receives the model’s requests and manages their execution. It validates tool invocations, calls external APIs, handles tool errors, and feeds the results back to the model in a standardized format.

Responsibilities of the MCP runtime include:

• Parsing and validating the model’s structured outputs
• Routing tool calls to the correct backend or API
• Logging and auditing tool usage
• Managing sessions, state, and timeout logic
• Ensuring security and access control per tool

This runtime acts as both the sandbox and the execution environment for agentic behavior.

3. Tools and APIs

Each tool is an externally defined function or API that can be invoked by the model. Tools are described using OpenAPI specifications, which define:

• Tool name and operation ID
• Description (used by the model to understand when and how to use it)
• Input and output schemas (for validation and structured prompting)

These tools can include:

• Retrieval APIs (e.g., search or knowledge base lookups)
• Action APIs (e.g., send email, post to Slack, call a webhook)
• System services (e.g., time, environment, authentication)

Since MCP uses OpenAPI, the same tool can be reused across models and environments without redefinition.

4. Context and Memory

MCP supports persistent context updates, allowing the model to write to and read from a memory store during its lifecycle. This is useful for long-running tasks, agent planning, or user-specific personalization.

Memory operations are triggered through structured messages like memory_write, context_update, or search_memory. These are routed to a backing store (e.g., vector DB or JSON blob store) that keeps state across turns or sessions.

5. Schema and Validation Layer

All interactions in MCP are schema-bound. Whether it’s a tool request, context update, or final response, each message adheres to a specific JSON schema defined in the MCP specification. This validation guarantees type safety, helps avoid prompt injection, and makes debugging easier.

Working Principles of MCP

The working principles describe how the system operates during a model interaction:

1. Turn-based Execution MCP interactions follow a loop:

• The model receives input and existing context
• It produces a structured output (e.g., request to call a tool)
• The runtime executes this request
• The result is returned to the model
• The model responds with either an observation, another action, or a final answer

This loop continues until the model terminates the session.

2. Intent Declaration via Structured Messages Instead of natural language prompts, the model produces structured intent objects:

• tool_use → Call a tool with specific parameters
• tool_result → Result of the executed tool
• context_update → Update contextual state
• observation → Share intermediate reasoning This makes model output machine-readable, safe, and traceable.

3. Decoupled Execution The model does not directly interact with APIs or memory. It only declares intent. The runtime controls what gets executed and how, ensuring a secure and observable boundary.

4. Stateless Model, Stateful System The model is stateless between turns — it does not remember previous messages inherently. The context and memory layers ensure that the model has access to long-term or session-specific state via runtime-provided context.

5. Standardization with OpenAPI and JSON Schema All tools are described in OpenAPI. Inputs and outputs follow JSON schema validation. This allows automated compatibility checks, structured prompting, and reuse across LLMs.

Communication Flow of MCP

The communication flow in MCP is a structured, loop-based cycle where the model and external systems interact through a standardized protocol. Every interaction happens as a series of turns, with the model producing an action and the runtime executing it before returning a result.

Let’s go through the flow, step by step:

Step 1: User Input or Initial Prompt

The interaction begins with a user query or an automated system instruction. This is passed to the MCP runtime, which prepares the input for the model. It includes:

• The user input
• Current session context (from memory or previous turns)
• Any recent tool outputs (if the model is mid-process)

Step 2: Model Receives Context and Thinks

The model receives this structured context. Based on it, the model:

• Processes the input
• Evaluates whether it has enough information to answer
• If not, it decides to use a tool or update memory

Step 3: Model Emits a Structured Action

Instead of responding in plain text, the model returns a structured JSON message that declares intent. Common message types include:

• tool_use: Call an external API/tool
• memory_write: Save something to memory
• context_update: Modify the runtime’s working state
• observation: Share internal reasoning
• final_response: End the session with an answer

Each of these has a defined schema. For example:

{
  "type": "tool_use",
  "tool_name": "get_weather",
  "parameters": {
    "location": "Delhi"
  }
}

Step 4: MCP Runtime Executes the Action

The runtime receives the message and acts on it:

• If it’s a tool_use, it looks up the tool’s OpenAPI spec
• Validates that the input parameters match the schema
• Executes the tool call (e.g., fetch weather from a REST API)
• Collects the response, wraps it as a tool_result

If it’s a memory_write or context_update, the runtime modifies internal state or memory store accordingly.

Step 5: Result Returned to the Model

The runtime returns a structured response to the model, such as:

{
  "type": "tool_result",
  "tool_name": "get_weather",
  "output": {
    "temperature": "38°C",
    "condition": "Sunny"
  }
}

This response becomes part of the next turn’s context.

Step 6: Model Evaluates Again

With the new result, the model now decides:

• Do I need another tool?
• Do I need to store this in memory?
• Is this enough to answer the user?

It then emits another action — or produces a final_response to complete the task.

Step 7: Loop Continues Until Task is Done

This turn-based loop continues until the model signals it’s done.

The full cycle looks like this:

• User query or system trigger
• Model produces structured intent
• Runtime executes that intent
• Runtime returns result
• Model consumes result and decides next step
• Repeat until session ends

[User Input]  

      ↓  

[Model Receives Input + Context]  

      ↓  

[Model Thinks]  

      ↓  

[Model Emits Intent]  

(e.g., tool_use, memory_write, context_update)  

      ↓  

[MCP Runtime Receives Intent]  

      ↓  

[Runtime Executes Tool/API]  

      ↓  

[Runtime Returns Result]  

(e.g., tool_result, memory_ack)  

      ↓  

[Model Thinks Again]  

(decides: next tool? respond? write to memory?)  

      ↓  

Repeat loop or terminate with final response

This structured flow ensures that models don’t "blindly" call tools or rely on raw prompts. Instead, every step is schema-bound, interpretable, and logged — making MCP workflows safe, modular, and inspectable.

Challenges in Adopting MCP

• Limited Model Support: Most models don’t emit structured messages like tool_use or memory_write natively. Adoption is limited to models designed for MCP or heavily customized.
• Runtime Complexity: MCP depends on a runtime that can validate, route, and manage all tool calls and memory. There’s no plug-and-play infrastructure — it has to be built or adapted manually.
• Tool Spec Overhead: Every tool requires an OpenAPI spec with strict schema definitions. For large toolsets, writing and maintaining these becomes tedious and error-prone.
• Latency from Multi-Step Turns: Each model-tool interaction is one full loop. Multi-step workflows require multiple turns — adding latency, especially in real-time applications.
• Debugging Requires Schema Familiarity: Failures often come down to schema mismatches or type errors. Debugging them demands a working knowledge of structured messaging and validation logs.
• Early Ecosystem Stage: Ecosystem support is minimal. There’s no standard open-source MCP stack yet, which limits portability and slows down early adoption.

Resources:If you’d like to dive deeper into the protocol’s origin and specification, check out the official resource here: Model Contex Protocol

Conclusion

MCP offers a structured foundation for enabling dynamic, tool-augmented workflows in language models. By introducing strict schemas, runtime validation, and turn-based interaction loops, it moves agentic systems beyond prompt-driven hacks into a safer, more modular design.

While adoption is still early and infrastructure challenges remain, the protocol sets a clear direction for how models can operate securely in complex environments. For developers and teams looking to build interpretable, action-taking AI systems, MCP represents a major step toward standardization and reliability.

Future improvements in model compatibility, open-source runtimes, and broader ecosystem support will determine how quickly MCP becomes a mainstream part of AI system design.