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Day2_Tools_and_Interoperability

README.md

Day 2: Agent Tools & Interoperability with MCP

Overview

Day 2 of the Google AI Agents Intensive explores how agents gain real-world functionality through tools and how the Model Context Protocol (MCP) standardizes their integration for scalable, secure, and interoperable systems.

Agents evolve from simple pattern predictors into autonomous systems by leveraging external tools, APIs, and structured communication protocols.

Learning Objectives

By the end of this session, you should be able to:

  • Understand how tools extend a foundation model’s perception and action abilities
  • Integrate tools into an agentic architecture using APIs and SDKs
  • Explore the Model Context Protocol (MCP) for tool standardization
  • Identify best practices in tool design, documentation, and error handling
  • Recognize enterprise and security implications of MCP deployments

Topics Covered

🔧 Tool Fundamentals

  • What defines a tool in AI systems
  • Difference between Function Tools, Built-in Tools, and Agent Tools
  • Examples from Google ADK and Gemini API

🧩 Best Practices

  • Clear tool documentation (name, description, parameters)
  • Describe actions, not implementations
  • Publish tasks, not direct API calls
  • Keep tools granular and concise
  • Validate inputs and outputs with schemas
  • Provide informative error messages

🌐 Model Context Protocol (MCP)

  • Solves the N × M integration problem by creating a unified standard
  • Core components: Host, Client, and Server
  • Communication via JSON-RPC 2.0 over stdio or Streamable HTTP
  • Defines entities: Tools, Resources, Prompts, Sampling, Elicitation, Roots

⚙️ Tool Definition in MCP

  • Structured using JSON schema fields:
    name, description, inputSchema, outputSchema, annotations
  • Supports hints like readOnlyHint, idempotentHint, and destructiveHint
  • Tools return structured or unstructured results with validation and error handling

Security & Enterprise Considerations

🚨 Key Risks

  • Dynamic Capability Injection – unauthorized tool updates
  • Tool Shadowing – malicious duplicate tools
  • Malicious Tool Definitions – deceptive or unsafe APIs
  • Sensitive Data Leaks – unfiltered user or enterprise data
  • Confused Deputy Problem – privileged MCP servers misused by low-trust models

🛡️ Mitigations

  • Enforce allowlists and version pinning
  • Validate inputs, outputs, and tool definitions
  • Use scoped credentials and principle of least privilege
  • Separate trusted/untrusted planners
  • Add human-in-the-loop checkpoints for critical actions

Daily Goals

  • Integrate at least 2–3 tools with an agent (e.g., API, search, or control function)
  • Build a custom tool to perform a defined user task
  • Understand the MCP host–client–server architecture
  • Document your integration steps in concepts.md

Resources

  • notebook_template.ipynb – for hands-on MCP experiments
  • Day_2_AI_Agents_Notes.md – detailed conceptual notes
  • Day_2_v6.pdf – official course whitepaper reference
  • ../Resources/ – supplementary materials and SDK documentation

Next Steps

Proceed to Day 3: Context and Memory
Learn how agents manage internal state, retrieve past information, and use memory for reasoning and decision-making in autonomous workflows.