Best Practices for Writing MCP Servers
An advanced, practical course on designing, implementing, securing, and operationalizing MCP servers for experienced developers building tools for AI agents, LLM applications, and RAG systems.
An intensive course for experienced programmers who know Python and the AI/ML/LLM ecosystem and want to design production MCP servers in line with the current specification and industry practices. The program focuses on hands-on implementation: modeling tools/resources/prompts contracts, choosing the transport (stdio, SSE, Streamable HTTP), security and OAuth authorization, ergonomics of input/output schemas, reducing the risk of prompt injection, observability, compatibility testing, and deployment to a production environment. The course takes into account the latest directions in the MCP ecosystem, including preference for official, compliant high-level SDKs, current requirements for associating server requests with client requests for sampling/elicitation/roots, and best practices for secure integration with clients such as ChatGPT, Claude Code, VS Code, and custom applications. Current official materials indicate, among other things, that Python and TypeScript are currently Tier 1 SDKs, the official Python SDK supports stdio, SSE, and Streamable HTTP, and for remote MCP servers, trusted provider servers are recommended, caution in data disclosure is advised, and OAuth 2.0/2.1 with dynamic client registration should be implemented wherever possible.
What you will learn
- Explain the MCP architecture and correctly separate responsibilities between tools, resources, and prompts.
- Design an MCP server in Python using the official Tier 1 SDK and choose the appropriate transport: stdio, SSE, or Streamable HTTP.
- Design stable, minimal, and secure input/output contracts based on JSON Schema for MCP tools.
- Implement secure authentication and authorization for a remote MCP server using OAuth 2.x, including proper handling of bearer tokens, HTTPS, and token rotation.
- Apply practices to reduce the risks of prompt injection, data exfiltration, and privilege abuse in MCP servers, taking into account the client-server trust model and human-in-the-loop for sampling.
- Correctly use sampling, elicitation, and roots in line with the latest direction of the specification: only as operations associated with an active client request.
- Add logging, tracing, metrics, limits, timeouts, retries, and idempotency so that the MCP server is operationally ready for production.
- Prepare contract and integration tests with MCP Inspector and a production deployment checklist.
Prerequisites
Excellent knowledge of Python 3.11+, HTTP/JSON, async programming (asyncio), FastAPI or similar web frameworks, basic OAuth 2.0, Docker, and practical experience with LLM/RAG/AI agents. The participant should understand the difference between tool calling, retrieval, and agent orchestration, and be able to read JSON Schema. Recommended: experience with integration tests, observability, and backend service deployment.
Course syllabus
- MCP without marketing: tools vs resources vs prompts in real AI agent use cases
- When should an MCP server be a thin layer over an API, and when should it be a domain facade
- Anti-patterns: one tool for everything, hidden agent logic, implicit side effects
- Quiz: recognizing a good MCP contract and incorrect responsibility boundaries
- Getting Started with FastMCP: creating a server, @mcp.tool(), @mcp.resource(), @mcp.prompt()
- Running with uv and streamable-http: a local developer loop without unnecessary ceremony
- Project structure: app/, domain/, adapters/, schemas/, auth/, observability/
- Async I/O, dependency injection, and separating the protocol layer from business logic
- Quiz: which elements should go into the MCP layer, and which into domain services
- JSON Schema for tool inputs: enum, defaults, validation, required fields, and error messages
- Designing outputs: json_response, result structures, status/error fields, and typed payloads
- Resource URI design: versioning, naming, cacheability and read semantics
- Prompts as a reusable interface: parameterization, guardrails, and reducing hidden dependencies
- How to write tool descriptions so models choose them correctly and don’t hallucinate parameters
- Quiz: refactoring poorly designed input/output schemas
- stdio for local developer tools vs Streamable HTTP for remote servers
- SSE and stream lifecycle: timeouts, keepalive, reverse proxy, load balancer, idle connections
- Streamable HTTP end-to-end with FastAPI/ASGI: routing, concurrency, backpressure
- Idempotency, correlation IDs, and request scoping for long-running operations
- Quiz: choosing the right transport for ChatGPT connector, Claude Code, and your own IDE agent
- Threat model for MCP: prompt injection, exfiltration, confused deputy, over-broad tools
- OAuth 2.x in MCP: bearer tokens, PKCE, token rotation, redirect URI validation, HTTPS everywhere
- Dynamic Client Registration and metadata discovery: when to implement them and how to reduce integration friction
- Least privilege in practice: scope per tool, tenant isolation, data minimization and audit trail
- Safe side effects: approvals, dry-run, idempotency keys, policy checks before executing an action
- Quiz: Security Incident Analysis on a Remote MCP Server
- Server-side API-keyless sampling: when it makes sense and when it’s better not to use it
- Human-in-the-loop and security policies for sampling/createMessage
- New request association rule: roots/list, sampling/createMessage, and elicitation/create only in the context of an active request
- Designing fallbacks when the client does not support selected capabilities
- Quiz: which interactions comply with the specification, and which violate the protocol model
- Contract tests for tool schemas and resources: snapshots, golden files, backward compatibility
- MCP Inspector and end-to-end integration tests with an MCP client
- Structured logging, OpenTelemetry traces, metrics per tool and latency budgets
- Rate limiting, retries, circuit breakers and degradation of dependent services
- Error handling: user-safe errors vs developer diagnostics, mapping exceptions to MCP responses
- Quiz: selecting metrics and alerts for a production MCP server
- Docker, CI/CD and release engineering for an MCP server: contract versioning and rollouts
- Publishing readiness: tool documentation, examples, changelog, SLA, and security policies
- Choosing official and trusted integrations: how not to build a server that cannot be used safely
- Checklist production readiness: auth, logs, traces, tests, quotas, data retention, incident response
- Final Quiz: Reference Architecture of an MCP Server for a Real AI Product
FAQ
This is an intensive program for experienced developers who know Python and the AI/ML/LLM ecosystem and want to design MCP servers ready for production deployment. The course will be especially valuable for people building agent integrations, developer tools, workflow automation, and secure data access layers.
Yes. The program was designed based on current MCP ecosystem practices, in which Streamable HTTP is the recommended transport for remote servers, while the older HTTP+SSE model is maintained mainly for backward compatibility. We also discuss the latest development directions, such as the growing importance of authorization, the official MCP Registry in preview, and specification changes that strengthen interoperability and security.
Participants implement tools, resources, and prompts contracts, design ergonomic input/output schemas, choose the appropriate transport, build an OAuth authorization layer, implement observability, compatibility tests, and prompt injection mitigation mechanisms. We emphasize architectural decisions that matter in production environments: interface stability, security, and diagnostics.
Absolutely. This is one of the key modules. We discuss the MCP trust model, OAuth authorization for remote servers, input and output data validation, least privilege, and prompt injection mitigation techniques. This is also important because the latest research publications from 2025 and 2026 point to real vulnerabilities related to the implementation and compliance of MCP servers.
Yes. The course shows when to use stdio in local integrations, how to understand legacy HTTP+SSE, and why modern remote deployments increasingly rely on Streamable HTTP. This makes it easier to make the right design decisions depending on the client type, network requirements, and session model.
Because MCP is quickly becoming the standard for integrating models with tools and data sources. The ecosystem is maturing: official SDKs are available, a server registry is being developed, and major players support an interoperable approach to agents and tool-use. It is a good time to go deeper than tutorials and learn practices that let you build maintainable servers, compliant with the specification and ready for protocol evolution.
- 40 hours
- Advanced
- Certificate on completion
- Access immediately after purchase
- Lifetime access and updates
30-day money-back guarantee