The Integration Problem
Every AI agent needs to interact with the outside world. Whether it is reading a database, calling an API, searching the web, or editing a file, the agent's usefulness is directly proportional to the tools it can access.
Before MCP, every tool integration was custom. If you wanted your agent to work with Slack, you wrote a Slack integration. If you needed it to query PostgreSQL, you wrote a database connector. If it needed to access Google Drive, another custom integration. Each one had its own authentication pattern, error handling, and data format.
This approach does not scale. Building and maintaining dozens of custom integrations per agent consumes enormous engineering effort. And when you switch AI providers or agent frameworks, those integrations often need to be rewritten from scratch.
MCP (Model Context Protocol) solves this problem.
What Is MCP?
MCP is an open protocol developed by Anthropic that standardizes how AI applications connect to external data sources and tools. Think of it as USB for AI — just as USB provided a universal interface for connecting any peripheral to any computer, MCP provides a universal interface for connecting any AI model to any tool or data source.
The protocol defines a standard way for:
- Tools: Actions the agent can take (send email, create ticket, query database)
- Resources: Data the agent can read (files, documents, database records)
- Prompts: Reusable instruction templates for common operations
How MCP Works
MCP follows a client-server architecture:
-
MCP Server: A lightweight process that wraps a specific data source or tool, exposing its capabilities through the MCP protocol. There is an MCP server for Slack, one for PostgreSQL, one for GitHub, and so on.
-
MCP Client: The AI application or agent framework that connects to MCP servers to discover and use their capabilities.
-
Transport Layer: MCP supports multiple transport mechanisms — local stdio for same-machine communication and HTTP with SSE (Server-Sent Events) for remote connections.
When an agent needs to use a tool, the interaction follows a clean pattern:
Agent → MCP Client → MCP Server → External Service
↓
Agent ← MCP Client ← MCP Server ← Response
The agent never needs to know the details of how a specific service works. It just calls the tool through the standard MCP interface, and the MCP server handles the translation.
Why MCP Matters for Agentic Engineering
1. Write Once, Use Everywhere
An MCP server for a tool works with any MCP-compatible AI application. Build a Salesforce MCP server once, and it works with Claude, GPT, open-source models, and any future AI system that supports the protocol. This eliminates the O(n*m) integration problem where n agents need to individually integrate with m tools.
2. Composability
Because MCP servers are independent, self-contained processes, you can compose them freely. Need an agent that can search your knowledge base, create Jira tickets, and send Slack messages? Connect three MCP servers and the agent has all three capabilities. Adding a new capability is as simple as connecting another server.
3. Security Through Isolation
Each MCP server runs as its own process with its own permissions. A server that reads your file system does not have access to your Slack workspace. This isolation model makes it straightforward to implement the principle of least privilege — each tool has only the access it needs.
4. Community Ecosystem
Because MCP is an open standard, anyone can build MCP servers. This has created a rapidly growing ecosystem of pre-built servers for popular services:
- Databases: PostgreSQL, MySQL, SQLite, MongoDB
- Communication: Slack, Email, Discord
- Development: GitHub, GitLab, Linear
- Productivity: Google Drive, Notion, Confluence
- Cloud: AWS, GCP, Vercel, Supabase
- Custom: Any REST API, GraphQL endpoint, or local tool
5. Local-First Privacy
MCP servers can run locally on your machine, meaning sensitive data never needs to leave your environment. Your agent can access local files, databases, and services without any data passing through external APIs. This is a significant advantage for organizations with strict data privacy requirements.
Building with MCP
Creating an MCP Server
An MCP server is surprisingly simple to build. Here is the skeleton of a server that exposes a tool for searching a knowledge base:
import { Server } from "@modelcontextprotocol/sdk/server";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio";
const server = new Server({
name: "knowledge-base",
version: "1.0.0",
});
server.setRequestHandler("tools/list", async () => ({
tools: [
{
name: "search_knowledge_base",
description: "Search the company knowledge base for relevant articles",
inputSchema: {
type: "object",
properties: {
query: { type: "string", description: "Search query" },
limit: { type: "number", description: "Max results", default: 5 },
},
required: ["query"],
},
},
],
}));
server.setRequestHandler("tools/call", async (request) => {
if (request.params.name === "search_knowledge_base") {
const results = await searchKnowledgeBase(request.params.arguments);
return { content: [{ type: "text", text: JSON.stringify(results) }] };
}
});
const transport = new StdioServerTransport();
await server.connect(transport);
The server declares its capabilities (tools, resources, prompts) and handles incoming requests. All the protocol mechanics — message framing, capability negotiation, error handling — are managed by the SDK.
Connecting MCP to Your Agent
Most modern agent frameworks support MCP natively or through plugins. The connection is typically a few lines of configuration:
{
"mcpServers": {
"knowledge-base": {
"command": "node",
"args": ["./mcp-servers/knowledge-base/index.js"]
},
"slack": {
"command": "npx",
"args": ["@mcp/slack-server"],
"env": { "SLACK_TOKEN": "xoxb-..." }
}
}
}
The agent framework discovers available tools from each connected server and can use them during task execution.
MCP in Production
At Snapsonic, we use MCP extensively in our agent deployments. Here are patterns we have found effective:
Gateway Pattern
For enterprise deployments, we often set up an MCP gateway that provides centralized authentication, rate limiting, and audit logging across all MCP servers. This lets us enforce security policies consistently while keeping individual servers simple.
Capability Groups
We organize MCP servers into capability groups — "research" (web search, knowledge base, document reader), "action" (email, Slack, ticket creation), and "data" (databases, CRM, analytics). This makes it easy to give agents the right level of access for their task.
Observability
Every MCP tool call is instrumented for logging and metrics. We track which tools agents use most, how long calls take, and what failure patterns emerge. This data drives both agent improvement and infrastructure optimization.
The Future of MCP
MCP is still evolving rapidly. Key developments to watch:
- Authentication improvements: Standardized OAuth and API key management across servers
- Streaming support: Real-time data feeds through MCP connections
- Multi-modal resources: Image, audio, and video data through the resource protocol
- Discovery: Automatic discovery and connection to available MCP servers in a network
The protocol's open nature means that improvements come from the entire community. As more organizations adopt MCP, the ecosystem of available servers grows, making every MCP-compatible agent more capable.
Getting Started with MCP
If you are building AI agents, MCP should be a foundational part of your architecture. Start with:
- Use existing MCP servers for common services (Slack, GitHub, databases) instead of building custom integrations
- Build MCP servers for your internal tools and data sources
- Design agents to discover and use tools through MCP rather than hard-coding tool interfaces
- Plan for composability — structure your MCP servers so they can be mixed and matched across different agents
Snapsonic builds production-grade AI agent systems using MCP and other open standards. Based in Vancouver, Canada, we help businesses across North America deploy interoperable, scalable agent architectures. Let's talk about your AI agent strategy.
Frequently Asked Questions
What is MCP (Model Context Protocol)?
MCP is an open protocol developed by Anthropic that standardizes how AI applications connect to external data sources and tools. It provides a universal interface — similar to how USB works for peripherals — allowing AI agents to access files, databases, APIs, and other systems through a consistent protocol.
How does MCP differ from traditional API integrations?
Traditional integrations require custom code for each tool-agent combination. MCP standardizes the interface so any MCP-compatible agent can work with any MCP server. This eliminates redundant integration work and makes it easy to add or swap tools without rewriting agent code.
Is MCP only for Anthropic's Claude model?
No. MCP is an open standard that works with any AI model or agent framework. While Anthropic developed the protocol, it is designed to be model-agnostic. MCP servers work with Claude, GPT, open-source models, and any system that implements the MCP client specification.
Can MCP handle sensitive data securely?
Yes. MCP servers can run locally, keeping sensitive data on-premises. Each server runs as an isolated process with its own permissions, supporting the principle of least privilege. Enterprise deployments can add authentication, encryption, and audit logging through gateway patterns.