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AI / Agentic AI Interview Questions

What is the Model Context Protocol (MCP)?

The Model Context Protocol is a standardized way for AI agents and applications to share tools, resources, and context, so agents can coordinate workflows across different systems more reliably.

  • Lets an agent discover what tools and resources are available from an MCP server, rather than needing them hardcoded
  • Uses JSON-RPC under the hood, supporting long-lived, stateful sessions with streaming results
  • Aims to solve the interoperability problem of siloed agents that can't easily share capabilities or context

Rather than every agent framework inventing its own way to expose tools, MCP gives agent developers and tool providers a common protocol to build against.

What problem does MCP primarily aim to solve?
What underlying protocol does MCP use for communication?

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More Related questions...

What is Agentic AI? What are the core components of an AI agent? Define the ReAct framework? What is Tool Use (Function Calling) in agentic AI? What are the types of memory used by AI agents? What is a Multi-Agent System? Define Chain-of-Thought reasoning? What is the Plan-and-Execute pattern? What is Reflexion (self-correction) in agentic AI? Describe the OODA loop as applied to AI agents? What is Retrieval-Augmented Generation (RAG)? What is Agentic RAG? What is the Model Context Protocol (MCP)? What are the popular frameworks used to build AI agents? What is a Human-in-the-Loop checkpoint? Define Prompt Injection in the context of AI agents? What is Context Poisoning? What is Tool Injection? Define an Orchestrator-Worker pattern? What is short-term memory in an AI agent? What is long-term memory in an AI agent? What is episodic memory in an AI agent? List the common risks associated with deploying agentic AI? What is Tree-of-Thoughts reasoning? What is an agent's "turn budget"? Describe LangGraph's role in agent architecture? What is Context Bloat in multi-tool agent systems? What is Grounding in the context of AI agents? What is the difference between Agentic AI and a traditional chatbot? What is the difference between ReAct and Plan-and-Execute patterns? Why does ReAct carry higher latency and cost than Plan-and-Execute? How does an AI agent use tool schemas to decide which tool to call? When should you use a hierarchical (cyclical graph) architecture instead of a single-loop agent? Why is a Human Approval Node inserted into sensitive agent workflows? What is the difference between RAG and Agentic RAG? How does Reflexion reduce compounding errors in agent execution? Why do MCP servers remain stateless in the recommended architecture pattern? What is the difference between MRKL-style routing and end-to-end LLM reasoning? How does an OAuth-based identity flow protect an MCP-connected agent session? Why does context bloat occur when multiple MCP servers are connected simultaneously? What is the difference between sequential, hierarchical, and concurrent multi-agent orchestration? How can prompt injection bypass human-in-the-loop guardrails in an agentic workflow? Why do enterprises prefer hardcoded state-transition graphs over freeform agent loops? When would you choose an SDK/microservice integration over MCP for tool access? Explain the execution flow of a ReAct agent handling a failed tool call? Explain the internal working of a Plan-and-Execute agent's planning phase? Explain the lifecycle of an agent's memory from short-term context to long-term storage? Explain the internal working of Tree-of-Thoughts reasoning compared to Chain-of-Thought? How can you optimize a multi-agent system to reduce token cost and latency? How do you troubleshoot an agent that repeatedly calls the wrong tool?
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