Model Context Protocol (MCP)

The open standard that lets AI assistants plug into any tool or data source.

11 sessions at the summit5 external resources

Overview

Model Context Protocol (MCP) is an open standard, originally introduced by Anthropic, that defines how LLM-based applications connect to external data sources and tools. Think of it as "USB-C for AI." Once a service exposes an MCP server, any MCP-compatible client (Claude Desktop, Kiro, Cursor, Cline, Q CLI) can use its tools, resources, and prompts without custom integration. AWS has published MCP servers for EKS, Aurora, Knowledge Bases, CloudWatch, Documentation, and more.

Key concepts

  1. MCP servers expose three primitives: tools, resources, and prompts
  2. Transport: stdio for local, SSE/HTTP for remote
  3. Capability negotiation between client and server
  4. Authentication and scoping — what the agent is allowed to do
  5. Composing multiple MCP servers in one assistant

Key AWS services

  • AWS Documentation MCP Server
  • EKS MCP Server
  • Aurora MCP Server
  • Bedrock AgentCore Gateway

Learn more — curated resources

Hand-picked official docs, foundational papers, and the best community guides for going deeper on this topic.

Sessions on this topic

11 sessions from the Summit covered this topic. Each is a self-contained mini-lesson.

  1. ANT301Advanced

    A practitioners guide to data for agentic AI

    In this session, gain the skills needed to deploy end-to-end agentic AI applications using your most valuable data. This session focuses on data management using processes like Model Context Protocol (MCP) and Retrieval Augmented Generation (RAG), and provides concepts that apply to other methods of customizing agentic AI applications. Discover best practice architectures using AWS database services like Amazon Aurora and OpenSearch Service, along with analytical, data processing and streaming experiences found in SageMaker Unified Studio. Learn data lake, governance, and data quality concepts and how Amazon Bedrock AgentCore and Bedrock Knowledge Bases, and other features tie solution components together.

  2. ARC305Advanced

    Transforming from SaaS to multi-tenant agentic SaaS

    Existing SaaS providers must determine how and where agents best fit into their offerings. Getting there requires organizations to transform existing IP and functionality into agent-powered experiences. This breakout will dig into the details of this transformation, examining the patterns, strategies, and techniques that can be used to introduce agents into an existing multi-tenant system. Well focus heavily on identifying the target agents, digging into how/where theyre built and introduced, how theyre integrated, and so on. Well also dig into how multi-tenancy lands in new agents, integrating with MCP servers, using RAG, applying tenant isolation, supporting onboarding, and on on.

  3. PRT201-SIntermediate

    Postman and the Future of AI-Driven API Development in 2026

    Software development has fundamentally changed in 2026, driven by vibe coding, AI agents, and RAG/MCP. APIs are the interface layer for AI systems to perform meaningful work. For this to succeed, your APIs must be discoverable, consistent, and usable by both developers and agents. Postman is now central to designing, managing, and iterating on your APIs to be sustainable in this new era.

  4. PRT216-SIntermediate

    Postman and the Future of AI-Driven API Development in 2026

    Postman and the Future of AI-Driven API Development in 2026 (sponsored by Postman, Inc)Software development has fundamentally changed in 2026, driven by vibe coding, AI agents, and RAG/MCP. APIs are the interface layer for AI systems to perform meaningful work. For this to succeed, your APIs must be discoverable, consistent, and usable by both developers and agents. Postman is now central to designing, managing, and iterating on your APIs to be sustainable in this new era.

  5. DEV202Intermediate

    AI Native Development: Strategies and Impact across Amazon and AWS

    AI Native Development: Strategies and Impact across Amazon and AWSAmazon and AWS have evolved beyond AI-assisted development to embrace AI Native practices, integrating AI as a partner throughout the software development lifecycle. Learn how their teams leverage AWS foundational tools including Kiro, and Amazon Bedrock. Discover effective Prompt Driven Development methodologies and grassroots adoption strategies from early champions. See how Amazon enables teams to provide AI with right context through strategic use of MCP, RAG and custom models trained on Amazon technical knowledge. Understand the culture transformation required across multi-thousand person organizations, where every role must evolve. Gain actionable insights to accelerate your AI Native journey.

  6. DEV314Advanced

    AI Native Development: Strategies and Impact across Amazon and AWS

    Amazon and AWS have evolved beyond AI-assisted development to embrace AI Native practices, integrating AI as a partner throughout the software development lifecycle. Learn how their teams leverage AWS foundational tools including Kiro, and Amazon Bedrock. Discover effective Prompt Driven Development methodologies and grassroots adoption strategies from early champions. See how Amazon enables teams to provide AI with right context through strategic use of MCP, RAG and custom models trained on Amazon technical knowledge. Understand the culture transformation required across multi-thousand person organizations, where every role must evolve. Gain actionable insights to accelerate your AI Native journey.

  7. DEV305Advanced

    Agents in the enterprise: Best practices with Amazon Bedrock AgentCore

    As organizations scale AI agent development, robust enterprise architecture patterns become essential. In this advanced session, we'll explore how Amazon Bedrock AgentCore enables teams to build modular systems using their preferred frameworks while sharing tools through MCP gateways. Learn about A2A collaboration, shared memory, identity-based access controls, and integrated observability. Discover practical strategies for secure runtime deployment, standardized tool integration, evaluation frameworks, and end-to-end monitoring. Leave with actionable insights to build secure, scalable agent infrastructures that balance centralized governance with team autonomy.

  8. ISV201Intermediate

    MCP on EKS: Xero's AI-Driven Developer Experience

    AI coding agents are transforming how developers build and operate modern cloud-native applications. With tools such as Kiro CLI, Kiro IDE, or any MCP-compatible AI coding assistant, developers are embracing AI to move faster and scale smarter. This session explores how MCP servers help developers streamline code generation, deployment, and debugging by embedding infrastructure awareness directly into the AI assistant. Learn how Xero is leveraging MCP to speed up development, simplify operations, and deliver more reliable containerized apps at scale. Xero will also share their success story using Kiro CLI, Prometheus MCP, EKS MCP, and AWS Knowledge Base MCP to identify and resolve Prometheus cost spikesslashing costs by 40%.

  9. TNC301Advanced

    Using Tools and Agents in Generative AI applications

    Join us for an engaging session on AI Agents and Tools in AWS, where well explore how to build intelligent, autonomous systems using Amazon Bedrock and open-source frameworks. Learn about function calling, ReAct patterns, and AWSs comprehensive agent platforms. Well dive into practical demonstrations using Strands and CrewAI, and discover how to leverage protocols like MCP and A2A for seamless tool integration and agent collaboration. Perfect for developers looking to create production-ready AI solutions.

  10. DEV206Intermediate

    AI Isnt Just for Developers: Using Kiro CLI & AWS MCP for Cloud Ops

    You cant turn your head sideways without seeing a slew of articles, blogs, or videos about AI, and most of them focus on developer tooling and writing code. But AI isnt just for developers. Its an incredibly powerful tool for operations folks, too.In this lightning talk, Ill share how I use Kiro CLI and the Kiro console with AWS Model Context Protocol (MCP) integrations for day-to-day cloud operations. From information gathering and log analysis to reporting and IAM policy interpretation, these tools help reduce cognitive load and speed up your output when working with AWS environments.Ill also discuss how I used Kiros spec-driven development approach to build a Python-based reporting tool, despite not being a software developer.This session is designed to make AI tooling feel approachable and practical for anyone working in AWS — not just developers.

  11. ISV214Intermediate

    Grounding AI Agents: How to give your AI real-world data with MCP

    Most AI agents fail not because of models, but because they cant access trusted external data. This session shows how InfoTrack used Model Context Protocol (MCP) to connect agents to authoritative data sources via a compliant and secured gateway.

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Non-obvious insights

From the Playbook

One sharp, contrarian insight per session — the things teams don't think of unprompted.

RAG retrieval quality is dominated by chunking strategy, not embedding model. Boring but true. Spend a week on chunk size, overlap, and semantic boundaries before you spend a dollar on a fancier embedder. ---ANT301 — A practitioners guide to data for agentic AI
The right pricing model for agentic SaaS isn't seats or usage — it's *outcome-based* (per resolved ticket, per successful workflow, per accurate document processed). Almost no SaaS has the billing infra to do this. Start the billing rebuild now, not after launch. Your finance team will become a bottleneck if you don't. ---ARC305 — Transforming from SaaS to multi-tenant agentic SaaS
The 40% Prometheus cost cut came from agents finding *cardinality bombs* humans missed. The gain wasn't AI being smarter — it was AI being patient enough to read every metric label. Use AI for tedious-but-tractable problems; that's where the 80% of the wins live. ---ISV201 — MCP on EKS: Xero's AI-Driven Developer Experience
ReAct (reasoning + acting) is the boring default that beats fancy multi-agent topologies for many tasks. Don't overcomplicate when ReAct will do. Most "we need multi-agent" assertions don't survive a careful look at whether ReAct would suffice. ---TNC301 — Using Tools and Agents in Generative AI applications
Ops engineers using AI tools become more *capable* than developers using AI tools — because ops tasks are highly structured and well-bounded. The AI productivity ceiling is higher in ops than in feature development. Underrated career bet for engineers in 2026. ---DEV206 — AI Isnt Just for Developers: Using Kiro CLI & AWS MC…
The MCP server's *logging* is more valuable than its data access. Audit trails for agent data access become regulatory evidence; design for that, not just functionality. Most teams build MCP servers and forget the audit log. ---ISV214 — Grounding AI Agents: How to give your AI real-world …