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Top 32 MCP Interview Questions

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MCP Theoretical Questions

Q1:   

What is Model Context Protocol?

  
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Q2:   

What are host, client, and server roles in MCP?

  
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Q3:   

What is the difference between an API and MCP?

  
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Q4:   

What is an MCP capability?

  
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Q5:   

How does MCP discovery differ from hardcoded integrations?

  
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Q6:   

How do MCP prompts differ from system prompts?

  
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Q7:   

What is the difference between tools, resources, and prompts in MCP?

  
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Q8:   

What is the difference between stdio and Streamable HTTP transports in MCP?

  
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Q9:   

Why is schema design important for MCP tools?

  
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Q10:   

What is the difference between MCP resources and RAG retrieval?

  
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Q11:   

How do completions help MCP clients?

  
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Q12:   

How does MCP handle long-running operations?

  
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Q13:   

How does MCP support agentic workflows?

  
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Q14:   

What are MCP notifications?

  
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Q15:   

How should MCP servers model authorization?

  
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Q16:   

What makes an MCP server trustworthy?

  
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Q17:   

What is elicitation in MCP?

  
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Q18:   

What are common MCP anti-patterns?

  
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Q19:   

How should you version MCP tools?

  
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Q20:   

What prompt injection risks exist in MCP?

  
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Q21:   

What is sampling in MCP and how is it different from tool calling?

  
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MCP Practical Challenges

Q1:   

How does MCP use JSON-RPC?

  
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Q2:   

How would you expose a resource template in MCP?

  
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Q3:   

How would you define a simple MCP tool?

  
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Q4:   

How should an MCP tool return errors?

  
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Q5:   

How should an MCP server validate tool input?

  
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Q6:   

How do roots help limit filesystem access in MCP?

  
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Q7:   

How would you secure a remote MCP server?

  
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Q8:   

How would you implement pagination for MCP list operations?

  
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Q9:   

How would you implement a property search MCP tool?

  
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Q10:   

How would you design an MCP interface for a real-estate product?

  
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Q11:   

How would you design an MCP server for an internal database?

  
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Prepare for AI agent developer interviews with 15 Model Context Protocol (MCP) questions covering tools, resources, prompts, JSON-RPC, transports, roots, sampling, security, and practical MCP server design....

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PyTorch popularity as a Deep Learning framework of choice is on the rise. As of December 2022, 62% of the academic papers were implemented in PyTorch whereas only 4% were for TensorFlow. Follow along and prepare effectively with these key 30 PyTorch ...
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Optimization algorithms are extensively used in training machine learning models. Data engineers employ algorithms like gradient descent, stochastic gradient descent, and variants (e.g., Adam, RMSprop) to optimize the model parameters and minimize th...
ChatGPT, an implementation of the GPT (Generative Pre-trained Transformer) model excels in understanding and generating human-like text, making it a powerful tool for NLP tasks. ML engineers and software developers can leverage ChatGPT's capabilities...