What is Model Context Protocol (MCP)?
Model Context Protocol (MCP) is an open standard introduced by Anthropic in November 2024 for connecting AI assistants (LLMs) to external data sources, tools, and services. MCP standardizes how LLM-powered agents discover and invoke external capabilities — replacing fragmented, vendor-specific integrations with a portable protocol.
Why MCP matters
Before MCP, every AI integration required custom plumbing: each LLM provider had its own function-calling syntax, each data source needed bespoke connectors. MCP creates a “USB-C for AI” — any MCP-compliant client (Claude Desktop, IDE assistants, agents) can use any MCP server (database connector, file system, API wrapper) without integration work.
MCP architecture
- MCP Server: Exposes capabilities (tools, resources, prompts) — typically wraps a specific service (Slack, Postgres, GitHub, custom API)
- MCP Client: AI application that consumes MCP servers (Claude Desktop, Cursor, custom agent frameworks)
- Transport: JSON-RPC over stdio or HTTP+SSE; OAuth 2.1 for authentication
- Three primitive types: Tools (function calls), Resources (data files/URLs), Prompts (templated workflows)
MCP vs OpenAI Function Calling vs LangChain
| Approach | Portability | Discovery |
|---|---|---|
| OpenAI Function Calling | Vendor-locked (GPT only) | Static schema in API call |
| LangChain Tools | Python-only, framework-dependent | Code-defined |
| MCP | Open, multi-vendor | Dynamic, runtime discovery |
2025 MCP adoption
- Anthropic Claude Desktop (native MCP)
- Cursor, Continue, Cline (IDEs)
- Block (formerly Square), Replit, Apollo, Sourcegraph adopted MCP early
- OpenAI added MCP support in early 2025
- Microsoft Copilot Studio integrating MCP servers
- 500+ public MCP servers (GitHub, Slack, Postgres, AWS, Salesforce, Linear, Notion, etc.)
Common MCP use cases
- IDE assistants: Read codebase, run tests, query DB without API-specific plumbing
- Customer support agents: Multi-tool agent calling Zendesk + Salesforce + internal docs
- Workflow automation: AI agents orchestrating Notion, Slack, Linear
- Internal tooling: Custom MCP servers for company-specific data + processes
Legal/compliance considerations
- Data flows: MCP server enables AI to access sensitive data — DPA + KVKK data processor obligations apply
- OAuth scope minimization: Grant only required permissions to MCP servers
- Audit logs: All MCP tool calls should be logged for compliance review
- AI Act (EU 2024): High-risk AI applications using MCP tools must document data flows
Practical implications for founders
If you’re building AI features: MCP is now the de facto standard. Don’t lock into OpenAI Function Calling; build MCP-compliant tools that can work across any LLM provider. For Turkish SaaS: expose an MCP server for your product — enables your app to be consumed by Claude/ChatGPT/Cursor without partnerships. Vircon Legal advises on data flow disclosure language for MCP-enabled products.