AI/ML
Model Context Protocol (MCP)
What Model Context Protocol is and why it matters for AI agents
Definition
Model Context Protocol (MCP) is an open-source standard created by Anthropic that standardizes how AI models and agents connect to external tools and data sources. MCP defines a client-server architecture where AI applications (MCP clients) communicate with data sources and services (MCP servers) through a uniform protocol. This eliminates the need for custom integrations and lets developers build tool connections once that work across any MCP-compatible AI application. MCP is supported by Claude, Cursor, Windsurf, and a growing ecosystem of developer tools.
How it works
Before MCP, every AI application had to build custom integrations for each external tool or data source it needed to access. If you wanted an AI assistant to read from your database, search your documents, and call your internal APIs, you needed three separate integration layers. MCP replaces this with a single protocol that any AI application can use to connect to any MCP-compatible server.
MCP uses a client-server architecture. The AI application runs an MCP client that discovers available servers and their capabilities. Each MCP server exposes a set of tools (functions the AI can call), resources (data the AI can read), and prompts (templates the AI can use). The protocol handles capability negotiation, request/response formatting, and error handling.
For developers, MCP means building a tool integration once and having it work across multiple AI platforms. An MCP server that connects to Slack works with Claude Desktop, with Cursor, and with any other MCP-compatible client. This is similar to how USB standardized hardware connections - before USB, every device needed a proprietary cable.
How 1Raft uses Model Context Protocol
We build custom MCP servers for clients who need their AI agents to interact with internal systems. In a healthcare project, an MCP server connects an AI assistant to the client's EHR system, allowing clinicians to query patient data through natural language. We also build MCP servers that wrap internal APIs, databases, and document stores, giving AI agents secure, controlled access to company data without exposing raw credentials or endpoints.
Related terms
AI/ML
Agentic AI
Agentic AI refers to AI systems that can plan, make decisions, and take actions autonomously to achieve a goal. Unlike simple chatbots that respond to one prompt at a time, agentic systems break complex tasks into steps, use tools, and self-correct along the way.
AI/ML
Large Language Model (LLM)
A large language model is a neural network trained on massive text datasets to understand and generate human language. LLMs power chatbots, content generation, code assistants, and most modern AI products.
AI/ML
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation is a technique that combines a language model with a searchable knowledge base. Instead of relying solely on what the model learned during training, RAG retrieves relevant documents first, then generates answers grounded in that specific data.
AI/ML
Prompt Engineering
Prompt engineering is the practice of crafting and optimizing the instructions given to a language model to get consistent, high-quality outputs. It is the most accessible and cost-effective way to improve AI application behavior without modifying the underlying model.
Related services
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