Large language models are good at reasoning over text, but they can’t see your CRM, query your database or open a ticket in your project tracker on their own. For years developers have filled that gap with custom integrations, developed one by one. Model Context Protocol (MCP) provides a more consistent approach. It explains what MCP is, how it works, where it fits in business and what to look for on security.
What is the Model Context Protocol ?
The Model Context Protocol is an open standard that defines how AI applications interact with external tools, data sources, and services. It was released by Anthropic in November 2024 and is now used by many AI platforms, IDEs and developer tools. In late 2025 it was released under open governance through the Linux Foundation’s Agentic AI Foundation.
MCP is often likened to a USB-C port for AI. You write an integration once as an MCP server, and any MCP-enabled app can use it. Without a common standard, each app requires its own custom connector.
MCP is not an agent framework, a model or a replacement for your APIs. It’s a protocol, a set of rules for how two pieces of software talk to each other. It is based on JSON-RPC 2.0 messages.
The Problem MCP Solves
Let’s say a company has 3 AI applications (a chat assistant, a coding assistant and an internal support bot) and they want each of those to connect to 5 systems: Postgres, Google Drive, Salesforce, Github and Slack. By wiring each app directly to every system, you’re building and maintaining up to 15 separate integrations, each with its own authentication, data formats and error handling.
This is often called M $\times$ N integration problem. MCP reduces it to about M+N. Each system has one MCP server and each AI app has one MCP client implementation
| Approach | Integrations needed (3 apps, 5 systems) | Maintenance burden |
|---|---|---|
| Custom point-to-point | Up to 15 | High: each one is bespoke |
| Model Context Protocol | 3 clients + 5 servers | Lower: shared interface |
Core MCP Architecture
MCP has a client-server architecture with three roles.
Host, Client and Server
- Host: The AI application the user interacts with, e.g., a desktop chat app, an IDE or a custom agent.
- MCP client: A host component that maintains a dedicated connection to an MCP server. A host is running four clients attached to four servers.
- MCP server: A program that provides certain capabilities (e.g., access to a database or a SaaS API) in the MCP format.
┌─────────────── HOST (AI application) ───────────────┐
│ LLM ◄──► MCP Client A MCP Client B │
└──────────────────┼──────────────┼───────────────────┘
│ JSON-RPC │ JSON-RPC
┌────────▼───────┐ ┌────▼────────────┐
│ MCP Server: │ │ MCP Server: │
│ CRM │ │ File system │
└────────┬───────┘ └────┬────────────┘
▼ ▼
CRM API Local files
The Three Server Primitives
MCP servers expose capabilities through three core building blocks:
| Primitive | Controlled by | Purpose | Example |
|---|---|---|---|
| Tools | The model (with user approval) | Functions that perform actions or computations | create_invoice, search_orders |
| Resources | The application | Read-only data used as context | A file, a database schema, a document |
| Prompts | The user | Reusable templates for common tasks | “Summarise this week’s support tickets” |
Servers may also offer features to clients. With sampling, a server can ask the host model for a completion. Elicitation allows a server to ask a user for additional input. Roots tell a server what files or directories it is allowed to operate within.
Links and Transport
MCP has two standard transports:
- stdio: The server is running as a local process on the user’s machine. This is good for file systems. And developer tools.
- HTTP Streamable: The server is remote and accessed via HTTP. This is great for SaaS and shared company services.
How Information Flows
- Init: The client and server exchange protocol versions and declare capabilities.
- Discovery The client asks what tools, resources and prompts the server has, e.g. tools/list.
- The host sends the descriptions of the tools to the model, which decides if a tool is useful.
- Execution Client sends a tools/call request. The server does the work, and returns back a structured result.
- Response: The model responds to the user with the answer.
A Simple Conceptual Example
Sales manager asks the internal AI assistant, “Which enterprise deals over ₹50 lakh are stuck this month? Create a follow-up task for each owner.
- The host is an assistant that is connected to the CRM MCP server.
- It has advertized two tools during discovery: search_deals(filters) create_task(owner, deal_id, note)
- The model makes a call to search_deals with the stage, value and date filters.
- The server queries the API of the CRM with its own credentials and returns five deals that match.
- The model produces five following tasks. The host shows them to the manager for approval and after confirmation it calls create_task five times.
- The assistant gives a summary and links to the new tasks.
The model does not interact directly with the CRM. It only sees what the server exposes and the host decides which actions require human confirmation.
MCP vs Traditional API Integrations
MCP is not a replacement for REST or GraphQL API. Most MCP servers are thin wrappers around existing APIs. The distinction is about who the interface is made for.
| Aspect | Traditional API integration | Model Context Protocol |
|---|---|---|
| Designed for | Developers writing fixed code paths | AI models choosing tools at runtime |
| Discovery | Read docs, hard-code endpoints | Server describes its capabilities dynamically |
| Reuse | One integration per app | One server, many compatible hosts |
| Context | Raw data only | Tools, resources and prompts in a shared format |
| Switching AI vendors | Often requires rework | Server stays the same |
Standardization helps because teams can reuse community or vendor-built servers, change model providers without having to rebuild integrations, and use consistent logging and governance across every connection.
MCP also costs money. It adds another layer for it to be secure and operate. Many descriptions of a tool can crowd a model’s context window. And a simple, fixed workflow might still be better off with a direct API call.
Practical Business Use Cases
- Databases: Enable analysts to ask questions in plain language against read-only replicas of Postgres, MySQL or a data warehouse.
- Document stores and file systems Provide assistants with scoped access to shared drives, contracts or policy libraries.
- SaaS apps: Integrate with tools like Slack, Notion, Jira or Google Workspace. Many vendors now are publishing official MCP servers.
- Development tools: Code assistants can read repositories, look at CI failures, query error-monitoring platforms, or run tests.
- CRM systems: Summarize accounts, update records and draft outreach with live pipeline data.
- Internal knowledge: Surface wikis, SOPs and support history so answers are based on company-specific knowledge instead of the model’s general training.
At Digitech Markit, we often recommend that businesses start with read-only use cases, such as search and reporting, before enabling tools that write or change data.
Security Considerations
MCP makes connections easier, so it can also expose more if it is set up carelessly. While the protocol provides building blocks, security is largely a matter of your implementation and deployment.
Authentication
The MCP specification provides an OAuth 2.1 based authorization framework for remote servers accessed over HTTP. Do this instead of handing out static API keys. Local stdio servers usually rely on environment credentials. Protect those credentials.
Permissions and minimal privilege
- Only give each server the credentials it needs. The reporting server should connect to the database with a read role.
- Limit which directories, projects or records can be reached by using roots and scoped tokens.
- For sensitive systems it is better to have separate read and write servers.
Tool Access and Human Review
The specification says that hosts should have a human in the loop for tool calls. Require confirmation before any destructive or financial actions, e.g. deleting records, sending emails, issuing refunds, etc.
Prompt Injection and data leakage
Content returned by a tool (e.g. an email, web page or ticket) may contain instructions written to manipulate the model. All tool output is data not to be trusted. Don’t allow access to private data, exposure to untrusted content, and the ability to send data outward to all occur together in a single unsupervised workflow.
Supply Chain
Code runs with your credentials on third party MCP servers. Install servers only from trusted publishers and pin their versions Review the permissions they are asking for Log every tool call for auditing
Present Capabilities vs Future Possibilities
What MCP does today: It gives a stable, versioned spec. There are official SDKs for several languages, including Typescript, Python, Java, C# and Go. It supports local and remote transports and OAuth based authorization. Big AI clients have a huge server ecosystem behind them.
What’s still evolving: There’s still a lot of work to be done on enterprise features like centralized server registries, fine grained policy controls, long running async tasks, more mature identity management, etc. Quality and security of community servers varies greatly. MCP also does not make an AI agent autonomous, or reliable on its own. That will still be model, the design of the tools and your guardrails.
Conclusion
Model Context Protocol provides a standard way for AI applications to find and use external tools, data and prompts. This replaces many fragile one-off integrations with re-usable servers. The value is practical: quicker integration, easier vendor switching, more consistent governance. These benefits can only be realized when deployed with strong authentication, least-privilege access and human supervision. Start small, with trusted servers and read-only access and grow as your controls mature. For teams that want to build AI into their operations, Digitech Markit can help you map the right MCP architecture to your business systems.
Frequently Asked Questions
Is Model Context Protocol only for Claude?
No, MCP is an open standard and is supported by many AI platforms, IDEs and agent frameworks. Any application can be built with MCP clients.
Does MCP replace my current APIs?
No. MCP servers typically wrap existing APIs and present them in a discoverable and usable way to AI models.
What is the difference between MCP resources and MCP tools?
Tools are actions the model can invoke, e.g., create record. Resources are read-only data that application provides as context like file or database schema.
Do I have to run MCP servers locally?
Not always. Local servers use stdio. Remote servers use streamable HTTP which is more appropriate for shared business services.
Is MCP Safe for Enterprise Data?
If you implement it carefully, it can be. OAuth, scoped credentials, read-only roles where possible, human approval for sensitive actions and audit logging.
What makes MCP different from function calling?
Function calling An ability of the model to call tools you have written in your own code. MCP standardizes the packaging, discovery and sharing of those tools across applications.
How do I start with MCP?
Try an existing MCP server with a compatible client. Then set up a small read-only server using an official SDK for an internal data source.