What is MCP (Model Context Protocol)?

An open protocol introduced by Anthropic for connecting AI assistants to external data sources, tools, and systems — providing a standard interface that any model client can use to interact with any MCP-compatible server.

Definition

The Model Context Protocol (MCP) is an open protocol introduced by Anthropic in late 2024 for connecting AI assistants to external data sources, tools, and systems. The design intent is similar to LSP (Language Server Protocol) for code editors: rather than each AI client implementing custom integrations to each data source, MCP defines a standard protocol where any compliant client can talk to any compliant server. An MCP server exposes tools (function calls), resources (data the model can read), and prompts (templates the model can use); an MCP client is the AI application that consumes these capabilities.

MCP adoption accelerated through 2025 and into 2026, with native support added to Claude Desktop, Cursor, Continue, Cline, Qwen-Agent, OpenAI Agents SDK, and most major agent frameworks. The protocol now has hundreds of community-built servers covering filesystem access, database queries, GitHub/GitLab integration, web search, browser automation, and many other common AI integration patterns. For teams building AI products, MCP has become the standard way to expose internal tools to the AI assistants their users employ.

Why It Matters

Before MCP, each AI client required custom integration to each data source — a quadratic explosion of integration work as both the number of clients and the number of sources grew. MCP collapses this to a single standard protocol, meaning a tool integrator builds once and reaches every MCP-compatible client. For end-users, this means the AI assistant of their choice can natively interact with their company's tools, internal databases, and proprietary systems through a uniform interface.

Key Takeaways

MCP is an open protocol for connecting AI assistants to external tools, data, and systems
Introduced by Anthropic; now broadly adopted across the AI assistant and agent ecosystem
Designed similarly to LSP — single protocol replaces N×M custom integrations
MCP servers expose tools, resources, and prompts; MCP clients consume them
Native support in Claude Desktop, Cursor, Continue, Qwen-Agent, OpenAI Agents SDK, and more

How Ertas Helps

Ertas-trained models can be used as the underlying LLM in any MCP-compatible client. After fine-tuning your model in Ertas Studio and deploying via Ollama or vLLM, the model can be wired into Claude Desktop, Cursor, or any other MCP-aware application — using the standard tool-use prompt formats that MCP servers exchange. For best results, fine-tuning data should include MCP-style tool-use traces so the resulting model handles structured tool calls reliably.