AI Context: MCP, RAG, & Tools Explained
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Understanding AI Context: MCP, RAG, Tools & Context Explained
Component Definitions
| Component | What It Is | Key Features | Example |
|---|---|---|---|
| Context | All information used by the LLM during generation | • Chat, user input, RAG, tool results • Bounded by token limit • Temporary session memory | "My name is Raj" remembered during session |
| MCP | Model Context Protocol — open-source protocol for LLM ↔ system interaction | • JSON-RPC 2.0 spec • 1,000+ MCP servers by early 2025 • Standardizes tool execution, resource access | Claude or GPT calls company CRM via MCP server |
| RAG | Retrieval-Augmented Generation — combines semantic search with LLM output | • Embeds user query • Searches vector DB • Injects relevant docs into context | LLM retrieves legal cases → summarizes |
| Tools | External APIs or code the LLM can run | • Accessed via MCP or native tool APIs (like OpenAI's function calling) • Enables live queries, code, search | getWeather("Hyderabad") fetches live data |
System Architecture
┌─────────────────────────────────────────────────────────────┐
│ CONTEXT WINDOW │
│ ┌────────────┐ ┌─────────────┐ ┌──────────────┐ │
│ │ User Input │ │ Retrieved │ │ Tool Results │ │
│ │ & History │ │ Documents │ │ (Live Data) │ │
│ └────────────┘ └─────────────┘ └──────────────┘ │
└─────────────────────────────────────────────────────────────┘
▲
│ Context feeds the model
▼
┌─────────────────────┐
│ LLM │
│ (Claude / GPT / │
│ Gemini / Mistral) │
└─────────────────────┘
│
┌───────────────┼───────────────┐
▼ ▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ MCP │ │ RAG │ │ Native APIs │
│ (Protocol) │ │ (Retrieval) │ │ / Services │
└──────┬──────┘ └──────┬──────┘ └──────┬──────┘
▼ ▼ ▼
┌─────────────┐ ┌──────────────┐ ┌──────────────┐
│ MCP Servers │ │ Vector DBs │ │ External APIs│
│ • Tools │ │ • Pinecone │ │ • Weather │
│ • Resources │ │ • Chroma │ │ • Search │
│ • Prompts │ │ • FAISS │ │ • Code Exec │
└─────────────┘ └──────────────┘ └──────────────┘
Component Relationships
| Component | Feeds Into | Purpose |
|---|---|---|
| Context | LLM | Holds all runtime inputs |
| MCP | Context via tool results | Standardized tool & data access |
| RAG | Context via retrieved docs | Domain-specific semantic enrichment |
| Tools | Context via live results | Real-time functionality (e.g., code, APIs) |
Current Industry Adoption
| Provider | Status |
|---|---|
| Anthropic | Creator & lead maintainer of MCP |
| OpenAI | Native function calling API; MCP support via community |
| Function calling capabilities in Gemini API | |
| Microsoft | MCP integrated into Azure OpenAI Studio and Foundry (Preview) |
Key Note: While MCP is gaining adoption, each provider also maintains their own tool calling mechanisms (like OpenAI's function calling API) alongside MCP support.