MCP Protocol: Building Agent Tool Integrations Training Course
The Model Context Protocol (MCP) is an open standard established by Anthropic that specifies how AI agents can discover, invoke, and retrieve results from external tools. It accommodates both stdio and HTTP transports and has been embraced by Claude Code, OpenClaude, and a rapidly expanding ecosystem of agent platforms.
This instructor-led, live training (available online or onsite) targets backend developers and platform engineers looking to construct MCP-compatible tool servers, integrate them with agent clients, and establish an internal MCP ecosystem within their organization.
Upon completion of this training, participants will be able to:
- Develop an MCP server in TypeScript or Python that exposes custom enterprise tools.
- Link MCP servers to Claude Code, OpenClaude, and other agent clients.
- Select between stdio and HTTP transports based on deployment requirements.
- Register and discover MCP tools within an internal tool registry.
Course Format
- Interactive lectures and discussions.
- Comprehensive exercises and practical application.
- Hands-on implementation in a live lab environment.
Customization Options
- To request customized training for this course, please contact us to make arrangements.
Course Outline
The Protocol Anatomy
- Why function calling alone is insufficient for complex agent ecosystems
- MCP primitives: tools, resources, prompts, and their JSON schemas
- Lifecycle of an MCP session: initialize, list tools, call, return, shutdown
- Comparing MCP to OpenAPI and GraphQL for exposing capabilities to agents
Building a Stdio MCP Server
- Scaffolding a TypeScript MCP server with the official SDK
- Defining tool schemas with Zod and generating runtime validation
- Implementing tool handlers that call internal REST APIs or databases
- Handling errors, partial results, and long-running tool execution
Building an HTTP MCP Server
- Upgrading from stdio to HTTP for remote deployment and load balancing
- Implementing authentication using bearer tokens and mTLS
- Graceful degradation when HTTP connections fail mid-session
- Deploying HTTP MCP servers behind Kong or nginx with rate limiting
Client Integration Patterns
- Registering an MCP server with Claude Code using the configuration file
- Connecting OpenClaude to multiple MCP endpoints simultaneously
- Writing a custom Python agent client using the MCP Python SDK
- Gracefully handling tool availability changes at runtime
Resource and Prompt Exposure
- Exposing read-only resources for agent context enrichment
- Creating parameterized prompt templates that guide agent reasoning
- Updating resources dynamically when underlying data changes
- Separating mutable tools from immutable resources for security clarity
Internal Tool Registry and Discovery
- Building a company-wide MCP registry with metadata and ownership tags
- Auto-discovery via DNS-SD or well-known endpoint files
- Versioning tools and deprecating old endpoints without breaking clients
- Cataloging tools with natural language descriptions for agent searchability
Enterprise Security Boundaries
- Implementing authorization checks inside tool handlers based on agent identity
- Using network segmentation to isolate high-risk tools from general agent access
- Sandboxing tool execution with seccomp and gVisor containers
- Logging every tool invocation for compliance and forensic analysis
Performance and Reliability Engineering
- Setting timeout policies per tool family: database, compute, and external APIs
- Implementing circuit breakers when downstream services are unhealthy
- Caching tool results to reduce redundant expensive computations
- Running MCP servers as sidecars versus standalone microservices
Interoperability Across Agent Platforms
- Testing MCP server compatibility with Claude Code and Continue.dev clients
- Handling transport negotiation differences between platforms
- Writing polyfill adapters for non-MCP agent frameworks
- Building a cross-platform tool marketplace inside the organization
Evolving the MCP Ecosystem Internally
- Collecting developer feedback on tool usefulness and accuracy
- Running quarterly tool audits and pruning obsolete integrations
- Onboarding new teams with self-service MCP server templates
- Contributing improvements upstream to the open-source MCP specification
Requirements
- Programming experience in TypeScript or Python
- Understanding of LLM tool calling and function-calling patterns
- Basic networking knowledge: HTTP, WebSockets, and JSON-RPC
Audience
- Backend developers creating custom tools for AI agents
- Platform engineers standardizing AI agent access to enterprise systems
- Solution architects designing AI tool ecosystems for corporate adoption
Open Training Courses require 5+ participants.
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