Why Does MCP Matter?
MCP solves the integration fragmentation problem for LLM applications. Instead of every AI tool requiring custom connectors to every data source, MCP creates a standardized ecosystem where any MCP-compatible client can access any MCP server. This shift from N×M integrations to a single protocol benefits everyone in the AI ecosystem.
Why MCP matters for end-users
With MCP, AI assistants can access your specific data (files, calendars, code, APIs, etc.), making answers and actions contextual and personalized. AI can work with your actual information rather than generic responses.
Real-world examples:
"Find all Jira tickets assigned to me this sprint and draft status updates." The assistant accesses your Jira instance via an MCP server and generates updates based on actual ticket progress
"Summarize last week's meeting notes from Drive and schedule follow-ups." The assistant reads the notes via a Google Drive MCP server and books events via a Calendar server
"What tasks are blocking our Q4 project and when is our next sync?" Using multiple internal tools (Google Drive, Calendar, Gmail, Slack), the assistant analyzes your meetings, tasks, emails, and priorities to give you a comprehensive answer
Why MCP matters for developers and vendors
Write once, deploy everywhere. With MCP, you implement a server once, rather than writing N different plugins or SDKs for each AI surface. This reduces development overhead while expanding reach.
The power of standardization:
A GitHub MCP server is immediately usable from Claude, Copilot Studio, Slack MCP Client, and dozens of other hosts.
Developers can generate MCP servers directly from existing OpenAPI specs, exposing specific endpoints as needed.
Unlike SDKs, where you typically expose everything, MCP servers let you curate which operations to expose. You can start with read-only endpoints, limit to specific resources, or create higher-level operations that combine multiple API calls.
When you update your MCP server, all connected clients immediately benefit—no need to coordinate SDK updates across multiple platforms.
Technical flexibility:
Generate servers automatically from your API or hand-craft specific tools
Mix generated schemas with custom logic for complex workflows
Control exactly what goes into the AI's context window (critical since all tool schemas consume tokens)
Why MCP matters for the ecosystem
MCP creates the foundation for network effects through standardization. The protocol's design enables clear boundaries, progressive adoption, and sustainable growth.
Ecosystem benefits:
Explicit capability negotiation: Servers and clients declare their capabilities; Clients can choose which server capabilities to use.
Authentication: Servers can implement their own authentication methods (such as OAuth 2.1) based on their security needs.
Progressive feature adoption: Start with basic tool calls, then add resources, prompts, and sampling as needed.
Discovery: Find and reuse MCP servers—An MCP server works with Claude, VS Code, and any other MCP client.
Beyond tool calls:
MCP doesn’t just call APIs. Servers can expose documentation, enhance reasoning capabilities, or offer any other functionality that benefits from standardized LLM interaction. One tool might help with "extended thinking," another might provide specialized knowledge access, all through the same protocol.
