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Introducing Lightweight MySQL MCP Server: Secure AI Database Access

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A lightweight, secure, and extensible MCP (Model Context Protocol) server for MySQL designed to bridge the gap between relational databases and large language models (LLMs).

I’m releasing a new open-source project: mysql-mcp-server, a lightweight server that connects MySQL to AI tools via the Model Context Protocol (MCP). It’s designed to make MySQL safely accessible to language models, structured, read-only, and fully auditable.

This project started out of a practical need: as LLMs become part of everyday development workflows, there’s growing interest in using them to explore database schemas, write queries, or inspect real data. But exposing production databases directly to AI tools is a risk, especially without guardrails.

mysql-mcp-server offers a simple, secure solution. It provides a minimal but powerful MCP server that speaks directly to MySQL, while enforcing safety, observability, and structure.

What it does

mysql-mcp-server allows tools that speak MC, such as Claude Desktop, to interact with MySQL in a controlled, read-only environment. It currently supports:

  • Listing databases, tables, and columns
  • Describing table schemas
  • Running parameterized SELECT queries with row limits
  • Introspecting indexes, views, triggers (optional tools)
  • Handling multiple connections through DSNs
  • Optional vector search support if using MyVector
  • Running as either a local MCP-compatible binary or a remote REST API server

By default, it rejects any unsafe operations such as INSERT, UPDATE, or DROP. The goal is to make the server safe enough to be used locally or in shared environments without unintended side effects.

Why this matters

As more developers, analysts, and teams adopt LLMs for querying and documentation, there’s a gap between conversational interfaces and real database systems. Model Context Protocol helps bridge that gap by defining a set of safe, predictable tools that LLMs can use.

mysql-mcp-server brings that model to MySQL in a way that respects production safety while enabling exploration, inspection, and prototyping. It’s helpful in local development, devops workflows, support diagnostics, and even hybrid RAG scenarios when paired with a vector index.

Getting started

You can run it with Docker:

docker run -e MYSQL_DSN='user:pass@tcp(mysql-host:3306)/' \
  -p 7788:7788 ghcr.io/askdba/mysql-mcp-server:latest

Or install via Homebrew:

brew install askdba/tap/mysql-mcp-server
mysql-mcp-server

Once running, you can connect any MCP-compatible client (like Claude Desktop) to the server and begin issuing structured queries.

Use cases

  • Developers inspecting unfamiliar databases during onboarding
  • Data teams writing and validating SQL queries with AI assistance
  • Local RAG applications using MySQL and vector search with MyVector
  • Support and SRE teams need read-only access for troubleshooting

Roadmap and contributions

This is an early release and still evolving. Planned additions include:

  • More granular introspection tools (e.g., constraints, stored procedures)
  • Connection pooling and config profiles
  • Structured logging and tracing
  • More examples for integrating with LLM environments

If you’re working on anything related to MySQL, open-source AI tooling, or database accessibility, I’d be glad to collaborate.

Learn more

If you have feedback, ideas, or want to contribute, the project is open and active. Pull requests, bug reports, and discussions are all welcome.

Scoped Vector Search with the MyVector Plugin for MySQL – Part II

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Subtitle: Schema design, embedding workflows, hybrid search, and performance tradeoffs explained.

Quick Recap from Part 1

In Part 1, we introduced the MyVector plugin — a native extension that brings vector embeddings and HNSW-based approximate nearest neighbor (ANN) search into MySQL. We covered how MyVector supports scoped queries (e.g., WHERE user_id = X) to ensure that semantic search remains relevant, performant, and secure in real-world multi-tenant applications.

Now in Part 2, we move from concept to implementation:

  • How to store and index embeddings
  • How to design embedding workflows
  • How hybrid (vector + keyword) search works
  • How HNSW compares to brute-force search
  • How to tune for performance at scale

1. Schema Design for Vector Search

The first step is designing tables that support both structured and semantic data.

A typical schema looks like:

CREATE TABLE documents (
    id BIGINT PRIMARY KEY,
    user_id INT NOT NULL,
    title TEXT,
    body TEXT,
    embedding VECTOR(384),
    INDEX(embedding) VECTOR
);

Design tips:

  • Use VECTOR(n) to store dense embeddings (e.g., 384-dim for MiniLM).
  • Always combine vector queries with SQL filtering (WHERE user_id = …, category = …) to scope the search space.
  • Use TEXT or JSON fields for hybrid or metadata-driven filtering.
  • Consider separating raw text from embedding storage for cleaner pipelines.

2. Embedding Pipelines: Where and When to Embed

MyVector doesn’t generate embeddings — it stores and indexes them. You’ll need to decide how embeddings are generated and updated:

a. Offline (batch) embedding

  • Run scheduled jobs (e.g., nightly) to embed new rows.
  • Suitable for static content (documents, articles).
  • Can be run using Python + HuggingFace, OpenAI, etc.
# Python example
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")
vectors = model.encode(["Your text goes here"])

b. Write-time embedding

  • Embed text when inserted via your application.
  • Ensures embeddings are available immediately.
  • Good for chat apps, support tickets, and notes.

c. Query-time embedding

  • Used for user search input only.
  • Transforms search terms into vectors (not stored).
  • Passed into queries like:
ORDER BY L2_DISTANCE(embedding, '[query_vector]') ASC

3. Hybrid Search: Combine Text and Semantics

Most real-world search stacks benefit from combining keyword and vector search. MyVector enables this inside a single query:

SELECT id, title
FROM documents
WHERE MATCH(title, body) AGAINST('project deadline')
  AND user_id = 42
ORDER BY L2_DISTANCE(embedding, EMBED('deadline next week')) ASC
LIMIT 5;

This lets you:

  • Narrow results using lexical filters
  • Re-rank them semantically
  • All in MySQL — no sync to external vector DBs

This hybrid model is ideal for support systems, chatbots, documentation search, and QA systems.

4. Brute-Force vs. HNSW Indexing in MyVector

When it comes to similarity search, how you search impacts how fast you scale.

Brute-force search

  • Compares the query against every row
  • Guarantees exact results (100% recall)
  • Simple but slow for >10K rows
SELECT id
FROM documents
ORDER BY COSINE_DISTANCE(embedding, '[query_vector]') ASC
LIMIT 5;

HNSW: Hierarchical Navigable Small World

  • Graph-based ANN algorithm used by MyVector
  • Fast and memory-efficient
  • High recall (~90–99%) with tunable parameters (ef_search, M)
CREATE INDEX idx_vec ON documents(embedding) VECTOR
  COMMENT='{"HNSW_M": 32, "HNSW_EF_CONSTRUCTION": 200}';

Comparison

FeatureBrute ForceHNSW (MyVector)
Recall✅ 100%🔁 ~90–99%
Latency (1M rows)❌ 100–800ms+✅ ~5–20ms
Indexing❌ None✅ Required
Filtering Support✅ Yes✅ Yes
Ideal Use CaseSmall datasetsProduction search

5. Scoped Search as a Security Boundary

Because MyVector supports native SQL filtering, you can enforce access boundaries without separate vector security layers.

Patterns:

  • WHERE user_id = ? → personal search
  • WHERE org_id = ? → tenant isolation
  • Use views or stored procedures to enforce access policies

You don’t need to bolt access control onto your search engine — MySQL already knows your users.

6. HNSW Tuning for Performance

MyVector lets you tune index behavior at build or runtime:

ParamPurposeEffect
MGraph connectivityHigher = more accuracy + RAM
ef_searchTraversal breadth during queriesHigher = better recall, more latency
ef_constructionIndex quality at build timeAffects accuracy and build cost

Example:

ALTER INDEX idx_vec SET HNSW_M = 32, HNSW_EF_SEARCH = 100;

You can also control ef_search per session or per query soon (planned feature).

TL;DR: Production Patterns with MyVector

  • Use VECTOR(n) columns and HNSW indexing for fast ANN search
  • Embed externally using HuggingFace, OpenAI, Cohere, etc.
  • Combine text filtering + vector ranking for hybrid search
  • Use SQL filtering to scope vector search for performance and privacy
  • Tune ef_search and M to control latency vs. accuracy

Coming Up in Part 3

In Part 3, we’ll explore real-world implementations:

  • Semantic search
  • Real-time document recall
  • Chat message memory + re-ranking
  • Integrating MyVector into RAG and AI workflows

We’ll also show query plans and explain fallbacks when HNSW is disabled or brute-force is needed.

Scoped Vector Search with the MyVector Plugin for MySQL – Part I

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Semantic Search with SQL Simplicity and Operational Control

Introduction

Vector search is redefining how we work with unstructured and semantic data. Until recently, integrating it into traditional relational databases like MySQL required external services, extra infrastructure, or awkward workarounds. That changes with the MyVector plugin — a native vector indexing and search extension purpose-built for MySQL.

Whether you’re enhancing search for user-generated content, improving recommendation systems, or building AI-driven assistants, MyVector makes it possible to store, index, and search vector embeddings directly inside MySQL — with full support for SQL syntax, indexing, and filtering.

What Is MyVector?

The MyVector plugin adds native support for vector data types and approximate nearest neighbor (ANN) indexes in MySQL. It allows you to:

  • Define VECTOR(n) columns to store dense embeddings (e.g., 384-dim from BERT)
  • Index them using INDEX(column) VECTOR, which builds an HNSW-based structure
  • Run fast semantic queries using distance functions like L2_DISTANCE, COSINE_DISTANCE, and INNER_PRODUCT
  • Use full SQL syntax to filter, join, and paginate vector results alongside traditional columns

By leveraging HNSW, MyVector delivers millisecond-level ANN queries even with millions of rows — all from within MySQL.

Most importantly, it integrates directly into your existing MySQL setup—there is no new stack, no sync jobs, and no third-party dependencies.

Scoped Vector Search: The Real-World Requirement

In most production applications, you rarely want to search across all data. You need to scope vector comparisons to a subset — a single user’s data, a tenant’s records, or a relevant tag.

MyVector makes this easy by combining vector operations with standard SQL filters.

Under the Hood: HNSW and Query Performance

MyVector uses the HNSW algorithm for vector indexing. HNSW constructs a multi-layered proximity graph that enables extremely fast approximate nearest neighbor search with high recall. Key properties:

  • Logarithmic traversal through layers reduces search time
  • Dynamic index support: you can insert/update/delete vectors and reindex as needed
  • Configurable parameters like M and ef_search allow tuning for performance vs. accuracy

Under the Hood: HNSW and Query Performance

MyVector uses the HNSW algorithm for vector indexing. HNSW constructs a multi-layered proximity graph that enables extremely fast approximate nearest neighbor search with high recall. Key properties:

  • Fast ANN queries without external services
  • Scoped filtering before vector comparison
  • Logarithmic traversal through layers reduces search time
  • Dynamic index support: you can insert/update/delete vectors and reindex as needed
  • Configurable parameters like M and ef_search allow tuning for performance vs. accuracy

What’s Next

This post introduces the foundational concept of scoped vector search using MyVector and HNSW. In Part II, we’ll walk through practical schema design patterns, embedding workflows, and hybrid search strategies that combine traditional full-text matching with deep semantic understanding — using nothing but SQL.

From MySQL to Oracle ACE Pro: A Milestone in My Database Journey

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I’m incredibly honored to share some exciting news—I’ve been recognized as an Oracle ACE Pro by Oracle!

This recognition is deeply meaningful to me, not just as a personal milestone but as a reflection of the ongoing work I’ve poured into the database community for over three decades. It’s also a reminder of how powerful open collaboration, curiosity, and mentorship can be in shaping both a career and a community.

What Is the Oracle ACE Program?

For those unfamiliar, the Oracle ACE Program recognizes individuals who are not only technically skilled but also passionate about sharing their knowledge with the wider community. It celebrates those who contribute through blogging, speaking, writing, mentoring, and engaging in forums or user groups.

The program has multiple tiers: ACE Associate, Oracle ACE, ACE Pro, and ACE Director. Each level reflects a growing commitment to community contribution and leadership. Being named an Oracle ACE Pro places me among a diverse, global group of technologists who are actively shaping the future of Oracle technologies—and open-source ecosystems alongside them.

From MySQL to ACE: A Journey Rooted in Community

My journey with data began over three decades ago, and it’s taken me across continents, companies, and countless events. My early days were steeped in MySQL—performance tuning, operations, scaling architectures—and I quickly discovered that the greatest impact didn’t come from just solving problems, but from sharing the solutions.

Since then, my path has included global roles in consulting, support, and engineering leadership. I’ve had the opportunity to speak at international conferences, publish books like the MySQL Cookbook (4th Edition), and contribute to countless community efforts in the MySQL and opensource database ecosystems.

Recognition such as Most Influential in the Database Community (Redgate 100) and MySQL Rockstar have meant a lot—but being named an Oracle ACE Pro is especially meaningful. It represents a bridge between the worlds of open source and enterprise and affirms that collaboration across ecosystems is not only possible—it’s essential.

What This Recognition Means to Me

This isn’t just about a title or a badge. To me, becoming an Oracle ACE Pro is about continuing the mission—to share what I’ve learned, amplify others doing amazing work, and give back to the communities that have shaped my path.

I’ve always believed that technical excellence must go hand in hand with generosity. Whether it’s mentoring a young DBA, helping a team scale their architecture, or writing about real-world database design challenges, the point has never been visibility—it’s always been about value.

And that’s what this recognition reflects: not just what I’ve done, but what I hope to keep doing for the next generation of data professionals.

Looking Ahead

This milestone energizes me even more to keep contributing—not just within the Oracle ecosystem but across the open-source database space. I’ll continue speaking at events, writing, mentoring, and building resources that help engineers build better, faster, and more resilient systems.

I’m also excited about promoting hybrid data architectures combining MySQL, opensource, and cloud-native technologies. This is where the industry is heading, and I’m committed to helping folks navigate that evolving landscape with clarity and confidence.

Gratitude and Community

I want to thank Oracle for running a program that not only recognizes technical contributions, but also community-driven spirit. And a heartfelt thank you to the MySQL community, open-source contributors, and peers I’ve had the privilege of working alongside over the years.

You’ve all helped shape my thinking, my work, and my growth. I stand on the shoulders of a global community, and this milestone belongs to all of us.

Let’s Stay Connected

If you’re building something, learning something, or just curious about databases, I’d love to hear from you. Whether it’s MySQL performance, opensource design, or data architecture strategy, reach out. Let’s keep learning, building, and sharing—together.

And if you’re interested in becoming part of the Oracle ACE community, feel free to ping me. I’m always happy to share what I’ve learned and help others navigate that journey.

A Note About What’s Coming

As part of my role and responsibilities as an Oracle ACE Pro, I’ll be launching a new series of technical blog posts in the coming months. These will explore cutting-edge topics including:

• AI/ML and LLMs (Large Language Models)

• Vector Search and database integration

• Real-world use cases at the intersection of AI and relational databases

These areas are rapidly evolving, and I’m excited to share practical, hands-on insights on how they tie into modern data architecture—especially within the Oracle and open-source ecosystems.

Disclaimer: The views and opinions I’ll be sharing in upcoming posts are my own and do not necessarily reflect those of Oracle or any other organization. Content will be independent, community-driven, and based on real-world experience.

Stay tuned—and if you have specific questions or topics you’d like to see covered, feel free to reach out!

Thanks for reading—and here’s to the next chapter in our database story.

Sailing Through Three Decades of Database Administration: Lessons in Resilience and Innovation

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Databases are the backbone of every data-driven application, a crucial element that fuels everything from simple web apps to complex enterprise systems. For over three decades, I have navigated the tumultuous waters of database administration, balancing technical intricacies with the often challenging dynamics of workplaces. My journey is not just one of keeping systems running but one of constant evolution—both in my career and in the technology I’ve used to build efficient and scalable databases.

The Early Days: Setting Sail

When I began my journey into database administration, the landscape was vastly different. Relational databases were becoming the cornerstone of digital infrastructure, but the tools and techniques we take for granted today were still in their infancy. Back then, designing a database was more about intuition and experience than adhering to well-defined best practices. It was like sailing uncharted waters, where the guiding stars were trial and error, persistence, and a touch of creativity.

MySQL and PostgreSQL were just emerging, promising a future of open-source solutions that could rival proprietary giants. I knew that these open-source databases were not just cost-effective alternatives but had the potential to evolve into robust, scalable solutions that could meet the demands of modern applications. I’ve had the privilege of working alongside both these databases from their early stages to their current iterations, witnessing firsthand how they’ve transformed the way we think about database design and management.

Navigating Database Design and Modeling: A Mastery in MySQL and PostgreSQL

As I worked through project after project, one lesson became crystal clear: the design and modeling of your database can make or break your entire system. Poorly designed databases lead to inefficiencies, performance bottlenecks, and often costly rework down the road. It’s akin to building a ship with weak foundations—you might stay afloat for a while, but you’re doomed when the first storm hits.

In my recent book, Database Design and Modeling with PostgreSQL and MySQL, I aim to guide readers through mastering the art of database design. This mastery goes beyond just creating tables and writing queries. It requires understanding the advanced concepts that keep systems running smoothly even when data size and complexity increase.

Normalization, for instance, often becomes essential for performance optimization. Indexing strategies are not just about speeding up queries but also managing trade-offs between read and write performance. Transaction management and concurrency control play critical roles in multi-user environments, ensuring data remains consistent even as users interact with the system simultaneously.

Scaling Databases: Preparing for the Storms Ahead

As data grows, scalability becomes a focal point. It’s one thing to manage a few gigabytes of data with ease; it’s quite another to manage terabytes or even petabytes of data without sacrificing performance. This is where techniques like sharding, replication, and load balancing come into play. They allow us to distribute workloads across multiple servers, ensuring that no single point of failure can bring down an entire system.

But scalability isn’t just about keeping your system running. It’s about preparing for the inevitable growth of your data and ensuring your database infrastructure can handle the storm.

Backup and recovery strategies are equally important. They act as your lifeboat when things go wrong—and things will go wrong in the world of databases. Without a solid recovery plan, you risk losing not only your data but also the trust of your users and stakeholders.

Integrating Databases with Modern Applications: Staying the Course

Databases don’t exist in isolation. They are integral parts of larger ecosystems connected to web and mobile applications that demand real-time, reliable data. Understanding how to connect, query, and secure your database in a modern web application environment is critical. With APIs and data layers becoming more complex, ensuring that your databases remain efficient and secure while supporting growing application demands is more important than ever.

The Future of Databases: New Horizons

While relational databases like MySQL and PostgreSQL remain essential, the database world is rapidly evolving. NoSQL databases have emerged as a popular solution for handling unstructured data, while cloud databases offer scalability and flexibility that on-premise solutions often can’t match. Integrating AI and machine learning into database systems is another frontier, opening up possibilities for smarter data management, predictive analytics, and automated optimization.

Staying ahead of these trends will be key to mastering database design and management in the years ahead. The ability to adapt to new technologies while maintaining a firm grasp on the foundational principles of database design will set the next generation of database administrators apart.

Final Thoughts: A Journey Worth Taking

Three decades in, I can say with certainty that database administration is not just a technical discipline—it’s an art. Like sailing through rough seas, it requires both skill and intuition. You must be prepared for the unexpected, adapt to changing conditions, and always keep an eye on the horizon.

Through my experiences, I’ve learned that the most successful database administrators are not those who avoid challenges but those who embrace them, using each storm as an opportunity to improve. And as we move into the future of databases, these lessons will only become more valuable. Whether you’re just starting your journey or are already deep into your career, there’s always more to learn, more challenges to face, and more opportunities to build something truly remarkable.

In Database Design and Modeling with PostgreSQL and MySQL, I aim to share these lessons and provide the tools you need to navigate your database challenges. Because at the heart of every successful system is a well-designed database—and the expertise to keep it running smoothly, no matter how rough the seas get.

I’m thankful to my co-author Ibrar Ahmed, a true professional and PostgreSQL expert. I would like to thank our publisher, Packt, for making this book possible and our primary editor, Tiksha Lad, product manager, Apeksha Shetty, and project manager,Aparna Nair , and the rest of the Packt staff. I also would like to send my gratitude to our technical reviewers Frederic Descamps , Naresh Miryala, and Seemanjay Ameriya. Not to forget our foreword author, Peter Zaitsev; we are grateful for his valuable time.

#designandmodeling #mysql #postgresql #opensource #databases #author