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 Duration 14 hours

Course Outline

Anatomy of the Protocol

  • Limitations of function calling alone in complex agent ecosystems
  • Core MCP components: tools, resources, prompts, and their associated JSON schemas
  • The lifecycle of an MCP session: initialization, tool listing, invocation, result return, and termination
  • Contrasting MCP with OpenAPI and GraphQL for exposing capabilities to agents

Developing a Stdio MCP Server

  • Setting up a TypeScript MCP server using the official SDK
  • Defining tool schemas with Zod to generate runtime validation
  • Coding tool handlers that interact with internal REST APIs or databases
  • Managing errors, partial outcomes, and long-running execution processes

Developing an HTTP MCP Server

  • Upgrading from stdio to HTTP to support remote deployment and load balancing
  • Implementing security via bearer tokens and mTLS authentication
  • Handling graceful degradation during mid-session HTTP connection failures
  • Deploying HTTP MCP servers behind Kong or nginx with rate limiting controls

Client Integration Strategies

  • Registering MCP servers with Claude Code via configuration files
  • Connecting OpenClaude to multiple MCP endpoints concurrently
  • Developing custom Python agent clients using the MCP Python SDK
  • Adapting dynamically to changes in tool availability during runtime

Exposing Resources and Prompts

  • Providing read-only resources to enhance agent context
  • Designing parameterized prompt templates to steer agent reasoning
  • Dynamically updating resources as underlying data evolves
  • Distinguishing between mutable tools and immutable resources for security clarity

Internal Tool Registry and Discovery

  • Creating an organization-wide MCP registry with metadata and ownership tracking
  • Facilitating auto-discovery through DNS-SD or well-known endpoint files
  • Managing tool versioning and deprecating outdated endpoints without disrupting clients
  • Cataloging tools with natural language descriptions to improve agent searchability

Enterprise Security Controls

  • Enforcing authorization checks within tool handlers based on agent identity
  • Isolating high-risk tools from general agent access using network segmentation
  • Sandboxing tool execution using seccomp and gVisor containers
  • Logging all tool interactions for compliance and forensic investigation

Performance and Reliability Engineering

  • Configuring timeout policies for different tool categories: databases, compute, and external APIs
  • Implementing circuit breakers to handle downstream service instability
  • Caching tool outputs to minimize redundant high-cost computations
  • Evaluating MCP server deployment as sidecars versus standalone microservices

Interoperability Across Agent Platforms

  • Verifying MCP server compatibility with clients like Claude Code and Continue.dev
  • Managing transport negotiation variations across different platforms
  • Developing polyfill adapters for non-MCP agent frameworks
  • Establishing an internal, cross-platform tool marketplace

Growing the Internal MCP Ecosystem

  • Gathering developer feedback on tool utility and accuracy
  • Conducting quarterly tool reviews to remove obsolete integrations
  • Onboarding new teams through self-service MCP server templates
  • Contributing enhancements back to the open-source MCP specification

Requirements

  • Proficiency in TypeScript or Python programming
  • Familiarity with LLM tool invocation and function-calling architectures
  • Foundational knowledge of networking protocols, including HTTP, WebSockets, and JSON-RPC

Target Audience

  • Backend developers creating bespoke tools for AI agents
  • Platform engineers standardizing access to enterprise systems for AI agents
  • Solution architects designing scalable AI tool ecosystems for corporate deployment

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