What is the Model Context Protocol (MCP)?
MCP acts as a standardized integration layer that enables AI applications like Claude or ChatGPT to communicate with other tools, external data sources, APIs, and AI agents in a consistent, secure, and structured way.
In practical terms, MCP replaces brittle prompt hacks and ad hoc integrations with a well-defined messaging format. Instead of stuffing every tool instruction into a prompt, developers can expose functionality through MCP servers, while AI models (via MCP clients) invoke those functions using structured calls.
Take this example: an AI agent is asked to write code for a specific business use case. To do this well, it needs to access your internal documentation, APIs, and maybe even previous codebases. MCP creates the AI agent with the communication layer that enables these interactions.
MCP was introduced by Anthropic (the AI company behind Claude) to bring order and reliability to interactions between AI assistants and external tools. But it's not tied to Claude. MCP integration is model-agnostic – it works across LLM ecosystems, including OpenAI's ChatGPT, Meta's Llama, IBM's Granite, and orchestration layers like nexos.ai.
By standardizing how models and systems communicate, MCP dramatically reduces integration overhead and improves the performance of AI applications.
How does Model Context Protocol (MCP) work?
MCP defines a standardized way for LLM-based applications and AI agents to call external tools, retrieve structured data, and take other actions. This is how a typical interaction plays out when a user engages with an AI app that supports MCP:
- 1.An MCP client sends a message to a host with a task. This can be a user prompt, an action request, or a function call.
- 2.The MCP host, often an LLM runtime like Claude's agent loop or nexos.ai's AI orchestration layer, interprets the task.
- 3.If the host needs help completing the task (e.g., it requires external information that wasn't in its training data), it uses the MCP server to call registered tools or data sources.
- 4.The results are returned to the LLM in a structured format, and the LLM uses that new context to continue reasoning.
MCP uses four types of messages to keep these interactions clean and predictable:
- Requests: Sent by the MCP client to the MCP server to ask for data or perform an action.
- Results: Sent by the MCP server in response to a valid request, carrying the outcome.
- Errors: Sent when something goes wrong, and the server can't fulfill the request.
- Notifications: One-way informational messages that require no response. These can be sent by either the client or server.
Each message in the MCP system is typed and validated, ensuring clarity, traceability, and security in every exchange. That's a significant upgrade from today's more chaotic prompt-injection or tool-calling setups.
Why is MCP important?
As LLMs move from single-shot prompt responders to multi-step AI agents, they need a reliable coordination protocol. LLM agents can decide when to fetch data, which tool to use, and how to integrate results into their reasoning process. But at scale, this kind of autonomy quickly breaks down, especially without a common framework.
Let's take a closer look at the LLM challenges that MCP addresses.
Solves the NxM problem
In traditional LLM systems, integrating N models with M external tools means building N×M custom connections, each with its own API keys, logic, and tool permissions. This doesn't scale.
MCP introduces a single, standardized interface for all model-tool interactions. Developers implement a tool once, and it becomes compatible with any MCP-compliant model or application. This dramatically reduces engineering overhead and simplifies tool reusability across projects.
Structures context-sharing
Without MCP, LLMs rely on "prompt stuffing" – manually cramming tool descriptions, memory, and user context into a single prompt. This is messy and error-prone.
With MCP, context is modular: function definitions, memory, and interaction history are passed as structured messages.
Enables secure function calling
Function calling, in which LLMs execute code or make API calls, is a huge step forward. But it also opens the door to security risks if not managed correctly.
MCP standardizes how function calling works. Instead of ad hoc instructions, the MCP server streams tool definitions directly to the model. This gives the LLM a clear context while limiting what it can access. The result is more predictable behavior and a reduced attack surface.
Supports retrieval-augmented generation (RAG)
MCP also plays a pivotal role in improving RAG in AI, where models fetch information from external data sources before generating responses.
By providing a consistent way to call retrieval tools, databases, or search APIs, MCP makes RAG systems more reliable, composable, and secure. This is critical for enterprise environments where LLMs must return grounded answers based on up-to-date information rather than just training data.
Bridges isolated models
In today's ecosystem, each LLM provider handles context and tool integration differently, which creates fragmentation.
MCP acts as a shared protocol layer, allowing models and external services to communicate regardless of vendor. This enables a "plug-and-play" environment, where AI apps can swap tools or models without custom integration code.
Enables enterprise-grade control and flexibility
MCP is also a key enabler behind modern all-in-one AI platforms for business like nexos.ai, which aim to unify and secure complex LLM workflows. By building on MCP, these platforms can offer:
- AI Gateway: Centralizing control over all enterprise AI operations in a unified orchestration layer.
- LLM Observability: Monitoring model actions, tool usage, and context flow across agents.
- AI Governance: Applying access controls, policy enforcement, and audit logging.
- AI Guardrails: Defining what models can do, when, and under which conditions
- Multi-LLM workspace: Running different models, like Claude, GPT-5, or Llama, with shared tools and context through a common interface.
MCP architecture
The Model Context Protocol follows a client-server architecture, drawing inspiration from well-established design patterns like the Language Server Protocol (LSP). Just as LSP standardized how code editors interact with programming language tooling, MCP aims to standardize how AI applications interact with tools, APIs, memory layers, and other external systems.
The MCP architecture separates responsibilities into three main components: MCP clients, MCP hosts, and MCP servers. Alongside these roles, MCP also defines a transport layer that governs how messages move between clients and servers.
MCP client
In the MCP ecosystem, all communication between the host and server is initiated and routed through the client. An MCP client is the application, user interface, or tool that sends tasks or requests to an LLM or orchestration layer. It can be as simple as a chatbot interface, or as complex as a system of multiple AI agents working together.
MCP clients are responsible for:
- Formatting user input into MCP-compliant messages
- Finding and using available MCP servers
- Sending structured requests to the host
- Displaying or acting on results from the MCP server or host
While each client connects to MCP servers individually, many clients can share the same host. That means MCP hosts can connect to and coordinate with multiple MCP servers at once, which allows them to access a broad range of tools and data sources in parallel.
Examples of MCP clients include:
- Claude AI assistant
- Slack bots using Claude's SDK
- nexos.ai's AI agent interfaces
- Firebase Genkit, Google's framework for building AI workflows with native MCP support
Clients don't need to manage tool execution themselves – they just package the intent. Execution is handled downstream, ensuring consistency and simplifying client-side logic.
MCP host
The MCP host is the central runtime that connects everything: it receives messages from the client, manages context, communicates with the language model, and coordinates tool usage through the MCP server. It's the central coordinator that manages context, memory, and tool delegation.
Hosts are responsible for:
- Translating user intent into LLM prompts
- Maintaining execution memory
- Managing turn-by-turn interactions
- Calling tools via the MCP server when needed
Most of the logic that defines an AI agent's behavior (when to call tools, how to respond, and how to update context) lives in the host. It serves as the decision layer that turns model output into purposeful, repeatable workflows.
MCP server
The MCP server is the backend service that provides access to MCP tools, resources, and external systems on behalf of the language model. When an LLM needs to retrieve information, perform an action, or interact with a system it doesn't natively understand, the host routes that request to an MCP server.
In the MCP ecosystem, servers are also how LLMs interact with real-world infrastructure. For example, developers can use them to connect inferencing endpoints from providers like OpenAI or IBM Granite, making these models interoperable through the MCP SDK.
MCP servers provide three types of functionality:
- Prompts are predefined templates that guide model behavior. These can be inserted programmatically or triggered through user actions (e.g., slash commands or menus).
- Resources are structured data (e.g., documents, database results, file contents, or commit history) that add context to the model's reasoning.
- Tools are executable functions that the model can call, such as triggering an API, running a script, writing to a file, or performing CRUD operations.
Examples of MCP servers include:
- GitHub MCP server
- Tool calling APIs for Claude or Gemini
- nexos.ai's Secure Function Store
- Custom endpoints for memory lookup, database queries, web access, etc.
Transport layer
The transport layer handles message exchange between MCP clients and servers. It ensures that messages conform to a common format during transmission, enabling structured, bi-directional communication.
When a client sends an MCP protocol message, it's first encoded into JSON-RPC. This allows complex MCP data structures to be transported reliably, along with clear expectations for how each message should be processed.
On the return path, the server sends responses or notifications in JSON-RPC format, which are then decoded back into MCP protocol messages for further processing.
MCP supports two transport methods:
- Standard input/output (stdio): Best suited for local environments. It provides fast synchronous communication, which is ideal for tools and services running on the same machine.
- Server-sent events (SSE): Designed for remote resources. SSE enables efficient real-time streaming of data from the server to the client over HTTP.
Security challenges of MCP servers
MCP brings structure and consistency to AI agent workflows, but it also changes the AI security surface. When LLMs are allowed to call tools, access live systems, or retrieve sensitive data, mistakes scale quickly.
The most common AI security risks associated with MCP adoption include:
- Over-permissioning. If MCP servers expose too many tools without proper limits, LLMs may intentionally or not call risky functions.
- Lack of authentication. Without strict authentication, any client can impersonate another to access internal tools or sensitive data.
- Injection and misuse. Even in MCP's structured format, malicious clients may attempt to inject context, misuse tools, or manipulate responses.
- Human oversight is still needed. When tools trigger irreversible actions, such as sending messages, modifying records, or executing transactions, human-in-the-loop controls are still essential.
To reduce risk, teams deploying MCP should treat it as part of their security infrastructure. At a minimum, this means:
- Enforcing authentication between clients, hosts, and servers
- Logging all tool calls, inputs, and outputs to support traceability and incident response
- Restricting tool visibility based on context, user role, or session purpose
- Using rate limits and action throttling to reduce the blast radius of model errors
- Enforcing strict type-checking and filtering untrusted content
MCP doesn't introduce entirely new risks, but it makes existing ones more visible and more manageable. If deployed carefully, it can actually serve as a structured enforcement layer rather than a vulnerability. But like any powerful interface, it requires deliberate guardrails to be safe at scale.
Future of Model Context Protocol (MCP)
While the protocol is still in its early stages, MCP support is growing fast, especially as AI engineers seek interoperability, modularity, and control in building agentic systems.
And it's not happening in a vacuum. The development of MCP aligns closely with broader AI trends, from secure AI agent orchestration to model-agnostic toolchains and composable AI platforms. As we see a shift toward more autonomous workflows, MCP is quickly becoming a key enabler.
Here's what's next for MCP:
- Secure interactions. Future MCP updates will include fine-grained permissioning, encrypted payloads, and clearer intent signals to protect against hallucinations and unsafe calls.
- Better authentication. One of the protocol's current limitations is how it handles authentication between previously unrelated clients and servers. Today, this often depends on either static pre-registration or Dynamic Client Registration (DCR), both of which have operational downsides. A proposed alternative is the use of client ID metadata documents – publicly hosted metadata files that serve as identity anchors.
- Live agent coordination. As multi-agent systems grow, MCP may evolve to support real-time agent collaboration with message threading, delegation protocols, and shared memory. This would unlock more advanced agentic AI use cases, where multiple AI agents cooperate to complete complex tasks, from enterprise automation to dynamic research assistants and beyond.
- Model interoperability. Longer term, MCP could serve as the protocol layer that enables cross-model context exchange between Claude, ChatGPT, Gemini, Llama, and other LLM ecosystems. This would allow different models to participate in the same workflow without relying on vendor-specific infrastructure.
For MCP to reach its full potential, several technical and governance challenges need to be addressed:
- Standardizing message formats across closed and open systems
- Preventing fragmentation between model ecosystems
- Managing tool registries with consistent versioning and trust policies
- Balancing openness with security and compliance
Still, if the ecosystem continues to align on shared standards, MCP has the potential to become the protocol layer for context-aware AI systems, just as HTTP was for the early web.
