MCP (Model Context Protocol)

Model Context Protocol (MCP) is an open standard developed by Anthropic that defines how AI models connect to external tools, data sources, and services. Think of it as a universal adapter that lets AI assistants plug into any compatible system without custom integration code.

Before MCP, connecting an AI to a new tool meant building custom integrations—different APIs, authentication methods, and data formats for every service. MCP standardizes this into a single protocol that any AI system can speak.

The result: build a tool connector once, and any MCP-compatible AI can use it.

MCP uses a client-server architecture with three main components:

MCP Hosts Applications that want to use AI capabilities (like Claude Desktop, IDEs, or custom apps). The host manages connections and coordinates between the AI and tools.

MCP Clients Protocol clients that maintain connections to servers. Each client handles the communication for one server connection.

MCP Servers Lightweight programs that expose specific capabilities:

• Resources: Data the AI can read (files, database records, API responses)
• Tools: Functions the AI can execute (send email, query database, create file)
• Prompts: Reusable templates for common interactions

The flow:

1. Host application starts an MCP server for a capability (like file access)
2. AI model discovers available tools and resources through the protocol
3. When needed, the model requests tool execution via MCP
4. Server executes the action and returns results
5. Model incorporates results into its response

Universal compatibility Instead of building N integrations for N AI models, you build one MCP server. Any MCP-compatible model can use it—Claude, GPT, open-source models, or custom deployments.

Separation of concerns

• Model providers focus on intelligence
• Tool developers focus on capabilities
• Application developers focus on user experience

Each layer can evolve independently.

Security by design MCP includes built-in patterns for:

• Permission scoping (what can the AI access?)
• User confirmation for sensitive actions
• Audit logging of all tool interactions
• Sandboxed execution environments

Local-first architecture MCP servers can run on your machine, keeping sensitive data local. The AI doesn't need cloud access to your files—it talks to a local MCP server that handles file operations.

File system servers Provide secure access to local files and directories. The AI can read, write, and search files through controlled interfaces.

Database connectors Connect AI to PostgreSQL, SQLite, or other databases. Query data, generate reports, or update records through natural language.

API bridges Wrap external APIs (Slack, GitHub, Salesforce) in MCP interfaces. The AI gets consistent access patterns regardless of underlying API differences.

Development tools Code execution, terminal access, Git operations. Power agentic coding workflows where AI can write, test, and commit code.

Knowledge bases Connect to documentation, wikis, or proprietary content. Enable RAG-style retrieval through standardized interfaces.

Using existing servers The MCP ecosystem includes pre-built servers for common tools:

• Filesystem access
• Git operations
• Slack, GitHub, Google Drive
• PostgreSQL, SQLite
• Web browsing and search

Install a server, configure permissions, and your AI can use it immediately.

Creating custom servers For proprietary systems or custom needs, build your own server:

from mcp import Server, Tool

server = Server("my-crm")

@server.tool()
async def lookup_customer(email: str) -> dict:
    """Look up customer details by email address."""
    return await crm_database.find_by_email(email)

@server.tool()
async def create_ticket(customer_id: str, issue: str) -> dict:
    """Create a support ticket for a customer."""
    return await ticketing_system.create(customer_id, issue)

The SDK handles protocol details—you just define what your tools do.

Composing capabilities Connect multiple MCP servers to create powerful agent systems:

• Email server + Calendar server + CRM server = AI executive assistant
• Code server + Git server + Testing server = AI developer

vs Custom function calling Function calling requires implementing tool execution in your application. MCP moves this to standalone servers that multiple applications can share.

vs LangChain tools LangChain tools are Python-specific and tightly coupled to the framework. MCP is language-agnostic and framework-independent.

vs Plugins (ChatGPT plugins) Plugins were cloud-hosted and required OpenAI approval. MCP servers can run anywhere—locally, in your cloud, or hosted publicly.

vs Direct API integration APIs vary wildly in authentication, formats, and conventions. MCP normalizes everything into consistent patterns the AI understands.

Principle of least privilege Only expose capabilities the AI actually needs. A documentation assistant doesn't need file write access.

Clear tool descriptions The AI reads your tool descriptions to decide when to use them. Be specific about what each tool does and when to use it:

"Search company knowledge base. Use when the user asks about company policies, procedures, or internal documentation. Returns relevant document excerpts with source links."

Handle errors gracefully Return clear error messages the AI can explain to users. Don't crash—return structured error responses.

Log everything MCP interactions create an audit trail. Log tool calls, arguments, and results for debugging and compliance.

Version your servers As capabilities evolve, version your MCP servers so clients can handle compatibility.

MCP represents a shift in how we build AI applications—from monolithic systems to composable, interoperable components. As adoption grows, expect:

• Marketplace of MCP servers for common tools
• Enterprise servers for business systems (SAP, Salesforce, ServiceNow)
• Specialized servers for industries (healthcare, legal, finance)
• Development tools built around MCP debugging and testing

The protocol is open and evolving. Contributions from the community shape its direction, making it a true standard rather than a proprietary lock-in.