AI & Machine Learning💻 Technical CourseLearnAspire Certified

Model Context Protocol (MCP): Build Production-Grade AI Infrastructure with Python, PostgreSQL, and OpenTelemetry

Name: Model Context Protocol (MCP): Build Production-Grade AI Infrastructure with Python, PostgreSQL, and OpenTelemetry
Price: 2999.00 INR
Availability: InStock

“Stop Hand-Wiring Agents. Build the MCP Infrastructure They Deserve.”

Replace hand-wired LLM database clients with a stateful, access-controlled, fully auditable MCP server that survives a SOC 2 review.

Advanced12h6 modules47 slides18 exercises24 quiz Qs

🔥 Launch Price — 63% off. Limited time.

₹2,999₹7,999

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✓6 modules of content
✓47 concept slides
✓18 practical exercises
✓24 quiz questions
✓Capstone project
✓LearnAspire certificate

Learning Outcomes

What you'll learn

→You will be able to implement a stateful MCP Python SDK 1.9.x JSON-RPC server that registers typed resource handlers against the Acme Financial Services customer_accounts schema and responds correctly to initialize, resources/list, and resources/read lifecycle messages

→You will be able to enforce per-agent row-level access control by propagating calling agent identity from MCP request context into asyncpg SET LOCAL role statements, so that asyncpg.exceptions.InsufficientPrivilegeError is the only mechanism by which an agent can be denied access — with no application-layer filtering that can be bypassed

→You will be able to instrument every MCP resource read with OpenTelemetry spans carrying agent_id, resource_uri, row_count, and pg_role attributes, exporting them to Grafana Tempo 2.4 so your security team can query exactly which rows each agent accessed in any 24-hour window

→You will be able to implement MCP sampling extensions so that underwriting agents delegate LLM completion requests through your MCP server rather than calling Anthropic directly — centralising token budgets, model version pinning, and completion audit trails in one place

→You will be able to validate your MCP server against a pytest-asyncio 0.23 contract test suite covering resource scoping, unauthorised access rejection (including the exact asyncpg.exceptions.InsufficientPrivilegeError propagation path), and OTLP span emission — producing a CI-ready test run that a new team member can execute without modification

The day after you finish

The day after completing this course, you will open your company's internal PostgreSQL service, create a new MCP Python SDK 1.9.x server module that registers a resources/read handler scoped to the calling agent's JWT identity, wire asyncpg SET LOCAL role to enforce your existing row-level security policies, attach an OpenTelemetry OTLP exporter pointed at your Grafana Tempo instance, and run your pytest-asyncio contract suite against it — replacing your current hardcoded SQL client with zero loss of functionality and a passing audit trail before your next standup.

Who this is for

Primary: Staff or Principal Platform Engineer (6–10 years) shipping LLM-powered features in production who needs to replace bespoke SQL-to-agent bridges with auditable MCP infrastructure
Secondary: AI Systems Architect designing internal tool-use layers for multi-agent pipelines and responsible for security review readiness
Tertiary: Senior Backend Engineer maintaining internal API gateways who will integrate with or review the MCP server layer produced by this course

Prerequisites

Production experience with async Python (asyncio, async/await, async context managers) — the course never defines these
Hands-on experience consuming LLM APIs using tool_use / function-calling patterns (OpenAI or Anthropic format)
Working knowledge of PostgreSQL query design, connection pooling, and JSON column handling
Familiarity with JWT or bearer-token authentication flows in REST API middleware

Curriculum

6 modules · full breakdown

🤖 Part of: AI Engineering Path

Step 1 — Foundations

→Step 2 — Core Skills

→Step 3 — RAG

→Step 4 — LangGraph RAG

→Step 5 — Agent Systems

→Step 6 — Production

→Step 7 — MCP

→Step 8 — Enterprise

← Previous: Step 6 — Production Next in path: Step 8 — Enterprise →

🏆

Capstone Project

Acme Financial Services MCP Server — Production Deployment with Row-Level Security, Sampling Delegation, and Audit Dashboard

Learners build and ship a complete MCP server in Python 3.12 using MCP Python SDK 1.9.x that exposes the Acme Financial Services customer_accounts and underwriting_documents PostgreSQL 16.3 schema. The server enforces per-agent row-level security via asyncpg SET LOCAL role, delegates LLM completions through MCP sampling extensions using the Anthropic Python SDK 0.28 against claude-3-7-sonnet-20250219, emits structured OpenTelemetry spans to Grafana Tempo 2.4 via OTLP, and is containerised with Docker Compose 2.27. The submission is validated by a provided pytest-asyncio 0.23 contract test harness run by automated grader — not a rubric.

What you'll deliver

A GitHub repository containing: (1) a runnable Docker Compose stack with the MCP server, PostgreSQL 16.3 with row_security enabled, and Grafana Tempo; (2) a pytest-asyncio contract test suite with all tests passing; (3) a Grafana dashboard JSON file for agent audit trail queries; (4) an Architecture Decision Record markdown file documenting the resource scoping design choices suitable for team wiki publication

Portfolio value

Design, secure, and operate a production MCP server that enforces agent identity through PostgreSQL row-level security, delegates LLM sampling with token budgets, and emits full observability to Grafana Tempo—proving mastery of enterprise AI infrastructure as a full-stack owner.