When connecting AI assistants with practical systems and data, custom implementations and vendor-specific proprietary connectors proliferate, making maintenance and security governance complex. With a standardized connection method, you can expand integration scope safely while suppressing development and operations burden.
This article provides an easy-to-understand explanation of MCP’s (Model Context Protocol) basic concepts, mechanisms, key features and usage benefits, implementation flow, and comparison with other methods.
Table of Contents
- Basic Concepts of MCP
- Architecture and Configuration
- Main Functions and Mechanisms
- Tools (Operation Execution)
- Resources (Information Reference)
- Prompts (Standard Request Templates)
- Sampling (Answer Reconsideration)
- Operations Design for Safe Utilization
- Business Benefits
- Representative MCP Server Examples
- Implementation Steps
- Supplement: Specific Configuration Methods for Developers
- Comparison with Other Methods
- Misconceptions and Points to Note During Implementation
- Summary
Basic Concepts of MCP
MCP is a “common set of rules” that safely and easily connects AI apps with external data and tools.
Think of it like a USB port for AI. By going through MCP, you can connect to various databases and services in a unified way.
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As a publicly available open standard specification, it can connect with a wide range of services without depending on specific vendors.
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Divided into roles of Host, Client, and Server, responsibilities are clear.
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Based on JSON format message communication with unified connection procedures.
This enables safe and efficient exchange of data and operations needed by AI.
Architecture and Configuration
Understanding MCP’s basic configuration clarifies what needs to be prepared and where.
| Component | Role | Representative Examples |
|---|---|---|
| Host | Execution environment that initiates and manages connections on the LLM app side | Claude, GitHub Copilot Chat |
| Client | Connector within Host that interacts with MCP servers | Claude Code’s MCP client |
| Server | Side that actually provides tools and data | GitHub, Google Drive, Slack |
This mechanism enables clear management of “which services AI is interacting with and in what scope.”
Main Functions and Mechanisms
MCP’s representative functions are as follows:
Tools (Operation Execution)
A mechanism where AI performs specific work within permitted scope on behalf of users.
Examples: Create tickets from inquiries, send PRs (Pull Requests, change proposals) after document review, save minutes from meeting content.
Resources (Information Reference)
A mechanism where AI can read specified information and utilize it for answers and document creation. Can be set to reference-only, not rewritable.
Examples: Read PDFs on file servers and organize key points, retrieve dashboard values and reflect in reports.
Prompts (Standard Request Templates)
Template frequently used standard instructions and formats for immediate recall.
Examples: “Sales email draft,” “Weekly report structure,” “Incident report format.”
Sampling (Answer Reconsideration)
A mechanism to request additional consideration or regeneration from AI.
Examples: Generate multiple proposals and revise until conditions are met, reconfirm supporting materials before finalization.
In addition to these functions, operations support mechanisms such as open standards, minimum privilege design, and execution log recording are also equipped.
Operations Design for Safe Utilization
To safely incorporate MCP into business operations, operations design is important in addition to functionality.
- Explicit Consent
Establish mechanisms for users to understand and approve what information AI accesses and what operations it performs.
- Grant Minimum Necessary Privileges
Keep access permissions to minimum necessary, not granting excessive scope.
- Logs and Auditability
Record “when, what was accessed, what was executed” for later verification.
- External Server Evaluation
Check third-party server providers and security standards beforehand.
By incorporating such design, MCP becomes not just a “convenient mechanism” but a foundation that can be used confidently in business.
You may also want to read
MCP (Model Context Protocol) Security Design and Operations Guide
Business Benefits
Organizing implementation effects from a business perspective yields the following:
- Integration Cost Reduction
Avoid connector proliferation, reusable with standard interfaces.
- Vendor Lock-in Reduction
Not dependent on specific vendors, easy to switch hosts and models.
- Operations/Governance Standardization
Permission management and execution logs can be controlled within protocol-common frameworks.
- Easy Migration from Experiment to Production
Easy to expand servers built in PoC (pilot implementation) to full-scale operations.
Representative MCP Server Examples
Understanding which fields have established servers makes it easier to select initial integration destinations.
| Category | Server Examples | Main Use |
|---|---|---|
| Collaboration/Files | Google Drive, Slack, Canva | Document retrieval/summarization, message sending/receiving, design editing |
| Development/Operations | GitHub, Vercel, Netlify, Sentry | Issue/PR operations, deployment, log analysis, monitoring |
| Automation Platform | Zapier, Workato | Cross-SaaS integration (safe connection with OAuth authentication) |
Implementation Steps
After understanding MCP’s overall picture, it’s important to grasp the flow when actually implementing. Below are basic steps for starting with minimal configuration.
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Decide on host (Claude, Claude Code, GitHub Copilot, etc.).
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Select servers to connect (Google Drive, GitHub, etc.).
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Determine operations method (local execution = stdio / remote connection = HTTP or SSE).
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Issue authentication credentials (OAuth, API keys, etc.).
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Verify operation with approval UI → Test in workspace.
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Establish permission scope and log management, gradually migrate to production.
By following this flow, you can migrate from safe, small-scale testing to production implementation with assured safety and operability.
Supplement: Specific Configuration Methods for Developers
Here’s a configuration example with Claude CLI.
Option 1: Local Server (stdio)
claude mcp add airtable --env AIRTABLE_API_KEY=YOUR_KEY \\ -- npx -y airtable-mcp-server-- (double hyphen) separates CLI options from server commands.
Option 2: Remote SSE Server
claude mcp add --transport sse linear <https://mcp.linear.app/sse>Option 3: Remote HTTP Server
claude mcp add --transport http notion <https://mcp.notion.com/mcp>Comparison with Other Methods
To understand MCP’s characteristics more clearly, comparing with conventionally used connection methods is effective. While custom APIs, plugins, and RPA have advantages, issues remain in management and expansion perspectives. Here we organize MCP and other methods across several axes.
| Method | Standardization Level | Implementation Burden | Management/Audit |
|---|---|---|---|
| MCP | High (unified specification) | Low to Medium (can leverage existing servers) | High (logs and permission management also standardized) |
| Company-specific API implementation | Low (case-by-case) | Medium to High | Low (mechanisms dispersed) |
| Plugins/Extensions | Medium (vendor-only) | Medium | Medium (vendor-dependent) |
| RPA/Screen Operations | Low (UI-dependent) | Medium | Low (reproducibility and audit challenges) |
| Webhook/Direct REST | Low (individual handling) | Low to Medium | Low (cross-cutting control requires separate design) |
As the comparison shows, MCP is an approach focused on “integration standardization” and “operations standardization.”
Misconceptions and Points to Note During Implementation
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Not “specific model-only” → Usable with multiple hosts.
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Not “automatically safe upon implementation” → Operations design (consent, permissions, logs) is essential.
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Does not “replace everything” → Can coexist with REST and Webhooks.
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Beware acronym confusion → MCP stands for “Model Context Protocol.” Different from communication standards or certification exam MCPs.
Summary
MCP (Model Context Protocol) is a foundation for safely connecting AI and business systems in a standardized way.
With clear role division of Host/Client/Server, functions like Tools/Resources/Prompts, and security principles, it achieves both development/operations efficiency and extensibility compared to conventional individual implementations.
When considering implementation, it’s practical to first try on a small scale and gradually expand while establishing permission and log operations. As a next step, inventory whether connected SaaS have MCP servers and begin designing pilot implementation (PoC).