Training course · Model Context Protocol (MCP) · SQL & Databases

Model Context Protocol (MCP) for SQL and Database Professionals

Build and connect MCP servers securely: tools, resources and prompts for Claude Code, Cursor, Copilot and your own AI agents.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Model Context Protocol (MCP) for SQL and Database Professionals” is a hands-on course for roles such as Database and SQL developers, Database administrators for PostgreSQL, SQL Server, Oracle or MySQL and Backend developers responsible for data models and ORM layers. Rather than learning Model Context Protocol (MCP) in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from SQL & Databases.

The Model Context Protocol is an open standard that gives AI applications a uniform way to access external tools, data sources and workflows. Instead of building separate integrations for every assistant, you implement an MCP server once and use it in Claude Code, Claude Desktop, Cursor, GitHub Copilot, VS Code and your own agents. In this two-day course you learn the host, client and server architecture, the three core primitives of tools, resources and prompts, and the stdio and Streamable HTTP transports, applying each of them directly in your own code.

On day one you use the official TypeScript or Python SDK to build an MCP server that exposes internal APIs, databases or files to AI assistants in a controlled way, and you test it with the MCP Inspector. Day two covers remote servers, OAuth-based authorisation, security risks such as prompt injection and tool poisoning, and operations, logging and governance in an enterprise setting. You learn to write tool descriptions that models use reliably, and you leave knowing how to integrate MCP servers safely into your development and agent landscape.

Databases are the memory of every application, and their performance, consistency and security determine whether entire systems succeed. AI coding assistants and agents support database developers and DBAs in writing complex queries, reading execution plans, designing schemas and index strategies and migrating between database platforms. They translate business questions into SQL, refactor sprawling stored procedures and generate test data. One principle still holds: a syntactically valid query is not necessarily a correct one, and an agent with write access to production data is a serious risk. In this course you learn to use AI productively with schema context, execution plans and tests, and to design access so that your data stays protected at all times.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Database and SQL developers
  • Database administrators for PostgreSQL, SQL Server, Oracle or MySQL
  • Backend developers responsible for data models and ORM layers
  • Data engineers and analytics engineers
  • BI developers working with complex reporting queries

Typical challenges we address

  • Slow queries and load spikes whose causes are hidden in execution plans
  • Sprawling stored procedures and triggers without documentation or tests
  • Risky schema changes and migrations on live systems
  • Different SQL dialects during platform changes or in multi-database estates

What you will be able to do

  • You explain the MCP architecture and choose between tools, resources and prompts for each use case.
  • You develop your own MCP server in TypeScript or Python and test it systematically.
  • You connect MCP servers to Claude Code, Cursor, Copilot and custom agents.
  • You run remote servers using Streamable HTTP and OAuth-based authorisation.
  • Optimise queries and indexes with AI support based on execution plans rather than guesswork.
  • Plan and test schema changes and database migrations using generated scripts and rollback strategies.
  • Design database access for AI agents securely, with least privilege, read replicas and traceable queries.

Course outline

Day 1

Module 1 Architecture and core concepts
  • Roles of host, client and server and the lifecycle of a connection
  • JSON-RPC messages, capability negotiation and notifications
  • Tools, resources and prompts: choosing the right primitive
  • Client features such as sampling, roots and elicitation at a glance
Module 2 Building your own MCP server
  • Setting up a server with the official TypeScript or Python SDK
  • Defining tools cleanly with JSON Schema for inputs and outputs
  • Exposing resources and resource templates for documents, records and configuration
  • Testing and debugging with the MCP Inspector and meaningful error messages
Module 3 Connecting to Claude Code, Cursor, Copilot and agents
  • Configuring local stdio servers and remote servers in clients
  • Project-level and user-level configuration, approvals and permissions
  • Writing tool descriptions that models reliably select correctly
  • Integrating MCP servers into custom agents built with the Claude Agent SDK or OpenAI Agents SDK
Module 4 Transport, authorisation and operations
  • Streamable HTTP, session management and stateless servers
  • OAuth-based authorisation, token validation and integration with existing identity providers
  • Deploying as containers, on serverless platforms or behind a gateway
  • Logging, monitoring, rate limiting and versioning tool interfaces

Day 2

Module 5 Security and governance
  • Threats: prompt injection via tool results, tool poisoning, confused deputy
  • Least privilege, confirmation requirements and sandboxing
  • Reviewing and approving third-party servers within the organisation
  • Data protection, logging of personal data and requirements under GDPR and the EU AI Act
Module 6 Practice lab: SQL & Databases
  • Optimising SQL queries
  • Designing and reviewing schemas
  • Refactoring stored procedures
  • Planning migrations safely
  • Translating dialects
  • Generating test data
Module 7 Data protection, quality & transfer to daily work
  • Databases often hold personal and business-critical data: you learn to give AI tools schemas and anonymised samples instead of production data, manage credentials securely and implement GDPR requirements such as data minimisation and deletion policies.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Optimising SQL queries

Have execution plans from EXPLAIN ANALYZE or Query Store interpreted, identify bottlenecks such as full table scans or poor join orders and get rewritten queries and suitable indexes with a clear rationale.

Designing and reviewing schemas

Derive normalised data models with keys, constraints and data types from requirements, review existing schemas for redundancy and missing integrity rules and generate ER diagrams.

Refactoring stored procedures

Explain large PL/SQL or T-SQL procedures, convert cursor loops into set-based logic, standardise error handling and add tests with frameworks such as tSQLt or pgTAP.

Planning migrations safely

Generate versioned migration scripts for Flyway or Liquibase, design zero-downtime strategies such as expand and contract and assess rollback paths as well as effects on locking and replication.

Translating dialects

Migrate queries, functions and data types between Oracle, SQL Server, PostgreSQL and MySQL, account for differences in date functions, sequences and transaction behaviour and test for identical results.

Generating test data

Create realistic synthetic datasets that respect constraints, distributions and edge cases without using personal production data, for load tests and development environments. Generators live in the repository under version control.

Possible hands-on projects

Slow query analyser

A script that processes the slow query log or statistics views, groups the most expensive queries and reports concrete index and rewrite suggestions.

Schema documentation generator

A tool that reads a database schema and produces searchable documentation with table descriptions, relationships and an ER diagram. It refreshes automatically after each migration.

Text-to-SQL assistant

A small web app with read-only access to a reporting database that turns business questions into checked SQL and shows both query and result transparently.

Prerequisites

Solid programming skills in TypeScript/JavaScript or Python and a basic understanding of HTTP APIs and JSON. Some experience using AI assistants in day-to-day development is helpful. You write SQL regularly, understand joins, aggregations and indexes and work with at least one relational database system.

Technical requirements

A laptop with Node.js or Python, Git and a code editor, plus access to at least one MCP-capable client such as Claude Code, Claude Desktop, Cursor or VS Code with Copilot.

Data protection & compliance

Databases often hold personal and business-critical data: you learn to give AI tools schemas and anonymised samples instead of production data, manage credentials securely and implement GDPR requirements such as data minimisation and deletion policies.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Tue, 24 November 2026 – Wed, 25 November 2026 Online Request
  • Thu, 3 December 2026 – Fri, 4 December 2026 Munich Request
  • Thu, 10 December 2026 – Fri, 11 December 2026 Online Request
  • Wed, 16 December 2026 – Thu, 17 December 2026 Stuttgart Request
  • Mon, 18 January 2027 – Tue, 19 January 2027 Online Request
  • Thu, 28 January 2027 – Fri, 29 January 2027 Berlin Request

Frequently asked questions

Why would I need my own MCP server?

Whenever AI assistants need access to internal systems such as ticketing tools, databases, documentation or line-of-business applications. Your own server defines precisely which actions and data are available and can then be reused across several clients. That avoids isolated one-off integrations and keeps access firmly under your control.

Which programming language does the course use?

You can choose between TypeScript and Python, both of which have official SDKs. The concepts are identical and all exercises are available in both versions. Developers working in other languages still benefit, because the protocol is language-agnostic and SDKs for other languages follow a similar structure.

How secure is MCP for enterprise use?

The protocol provides mechanisms for authorisation and permissions, but real-world security depends on implementation and operations. Risks such as prompt injection through tool results or over-privileged servers are genuine. We cover concrete countermeasures, approval processes for third-party servers and how to handle personal data.

Should an AI agent access our database directly?

It can, but it needs careful planning. We recommend a dedicated read-only user, access to replicas or development databases rather than production, query timeouts and logging of every statement. Write changes go through versioned migration scripts with review. In the course you build exactly this safeguard, for instance through a restricted MCP server.

How reliable is AI-generated SQL?

It is usually syntactically correct, but semantic quality depends heavily on context: without the schema, relationships and business definitions, wrong joins or double counting creep in easily. With schema context, example queries and tests against known results, reliability rises considerably. You learn to validate generated queries systematically.

How much does “Model Context Protocol (MCP) for SQL and Database Professionals” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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