Author Archives: askdba

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Born to Sail, Forced to Work!

MySQL MCP Server v1.7.0 is out

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April 19, 2026

It took three release candidates and more CI tweaks than I’d like to admit, but v1.7.0 is finally tagged GA. Here’s what actually changed and why it matters.

The thing I kept getting asked about: add_connection

Almost every multi-database user hits the same wall: you configure your connections at startup, and that’s it. Want to point Claude at a different instance mid-session? Restart the server. Not great.

add_connection fixes that. Enable it with MYSQL_MCP_EXTENDED=1 and MYSQL_MCP_ENABLE_ADD_CONNECTION=1, and Claude can register a new named connection on the fly — DSN validation, duplicate-name rejection, and a hard block on the root MySQL user all happen before the connection is accepted. Once it’s in, use_connection it works as usual.

It’s intentionally opt-in behind two flags. Allowing an AI client to register arbitrary database connections at runtime warrants an explicit “yes, I want this” from the operator.

Finding stuff across a big schema: search_schema and schema_diff

Two tools I personally felt the absence of every time I was debugging a large schema.

search_schema does what it sounds like — pattern-match against table and column names across all accessible databases. Before this, you’d either write the query yourself or ask Claude to guess where a column lived. Now you just ask.

schema_diff is the one I’m more excited about. Point it at two databases, and it tells you what’s structurally different. Columns that exist in staging but not prod, type mismatches, missing indexes — all surface immediately. We’ve already caught more than a few “oh, that migration never ran” moments with it.

Pagination, retries, and the unglamorous stuff

run_query now supports an offset parameter for SELECT and UNION queries, returning has_more and next_offset in the response. Big result sets no longer mean hitting row caps and wondering what you missed.

Retries got a proper implementation too. Transient errors — bad pooled connections, deadlocks, lock wait timeouts — now trigger exponential backoff instead of just failing. After a driver.ErrBadConn the pool is re-pinged, which cuts recovery time noticeably after a MySQL restart.

Neither of these is flashy, but they’re the kind of thing that makes the tool feel solid rather than fragile.

Column masking

Set MYSQL_MCP_MASK_COLUMNS=email,password,ssn and those columns are redacted in every run_query response. Nothing leaves the server. No query rewrites, no application changes. It’s a small feature that a few teams have been asking for since before v1.6.

One breaking change worth knowing about: SSH host key verification

This one could bite you on upgrade if you’re using SSH tunnels. Host key verification is now on by default. The tunnel checks ~/.ssh/known_hosts (or MYSQL_SSH_KNOWN_HOSTS, or a pinned MYSQL_SSH_HOST_KEY_FINGERPRINT) before allowing the connection.

If you were running without strict host key checking, your tunnel will fail after upgrading until you either add the host key to known_hosts or explicitly opt out with MYSQL_SSH_STRICT_HOST_KEY_CHECKING=false. The opt-out exists, but it’s a MITM risk — the default is the right behavior.

Upgrading

# Homebrew
brew update && brew upgrade mysql-mcp-server
# Docker
docker pull ghcr.io/askdba/mysql-mcp-server:latest

Full changelog: github.com/askdba/mysql-mcp-server/releases/tag/v1.7.0

Questions and issues are welcome on GitHub.

Navigating Business Challenges: Insights from Sailing

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Some experiences can be simulated.

Sailing cannot, and it ever changes.

At first glance, a sailboat feels like leisure, wind, sea, and escape from reality. But the moment you take responsibility on board, that illusion disappears.

A sailboat is not an escape from life. It is a lifestyle full of responsibilities.

As we enter the sailing season, I wanted to take a few notes on how sailing aligns with the business life we live every day.

A Boat Is an Organization

A sailboat is, in essence, a fully functioning micro-organization.

  • The wind represents external market forces, unpredictable, uncontrollable, yet decisive.
  • The route is your strategy, chosen deliberately, but constantly under pressure to adapt.
  • The crew is your organization, each role critical, each mistake amplified.
  • The captain embodies leadership, not authority, but responsibility.

Unlike corporate environments, where feedback loops are often delayed or diluted, sailing delivers immediate and unambiguous consequences. A poor decision is not debated; it is felt. Instantly, no regrets, only lessons learned.

Reality Has No Buffer

At sea, there is no delay.
In most environments, reality is delayed through reports, meetings, and layers of interpretation. At sea, there is no such delay. If you are wrong, the boat tells you immediately. If communication fails, the system breaks immediately. There are no explanations, only outcomes.

If you’re wrong, the boat tells you immediately. If communication fails, the system breaks immediately.

No explanations. Just outcomes.

Leadership Is Behavior

On land, leadership can hide behind structure. At sea, it can’t. I sometimes say me, myself, and I, even though I have a crew with me.

I often just smile and don’t say anything.

There are no layers, no escalation paths, no time to reframe the narrative. Sometimes, the best and life-saving advice is to gear up with your life jacket if you haven’t already.

Only:

  • What you see and its relevance to your experience.
  • What you decide. There’s no right or wrong at sea. What you decide is what you’ll have to face.
  • What you communicate. There are a lot of assumptions; avoid them. Be clear and concise.

Teamwork Is Binary

Collaboration is not a “nice to have”; it either works or it doesn’t.

A maneuver with:

  • Slight hesitation
  • Unclear communication
  • Assumptions instead of alignment

will have consequences.

Nothing else can be more harmful than a precise decision.

Adapt or Stall

Plans don’t survive contact with reality. Wind shifts. Conditions change severely. You don’t debate it.

You adapt conditions continuously.

The Hidden Lesson

After a few hours, something shifts in people.

Less talking.

More observing.

Clearer communication.

Because reality leaves no room for ego.

Why It Matters

Sailing compresses how real systems behave:

  • Immediate feedback
  • Clear cause and effect
  • Shared exposure

It teaches what most environments dilute:

  • Decision-making
  • Communication
  • Accountability
  • Adaptation

But more than anything, it gives you something rare:

Final Thought

A sailboat is one of the few environments where there is no AI involved.

Only human, trained coordination.

That is exactly why it works.

Not as an escape, but as a mirror of how we actually operate under pressure.

Follow the adventure at @svrubato

v1.26.3

MyVector v1.26.3: Maintenance, CI, and Readiness for MySQL 9.7

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In my recent series on Scoped Vector Search, we looked at the query patterns that make vector search a first-class citizen in MySQL. While the logic for those searches is now established, the infrastructure supporting them requires constant attention as the MySQL ecosystem moves toward its new release model.

Today, I’m announcing MyVector v1.26.3. This is a foundational release focused on environment compatibility and CI/CD robustness.

What’s in v1.26.3?

This release ensures that MyVector remains stable and buildable across the shifting landscape of MySQL Innovation and LTS releases.

  • MySQL 8.4 & 9.6 Compatibility: We’ve updated the component sources and build logic to align with the headers and requirements for MySQL 8.4 (LTS) and the 9.6 Innovation release.
  • Ready for 9.7: The build system has been adapted to handle the upcoming 9.7 release, ensuring that users can transition to the next Innovation branch without delay.
  • Modernized Release Workflow: We’ve bumped our GitHub Actions (softprops/action-gh-release) from v1 to v2. While invisible to the user, this ensures our release pipeline remains secure and compatible with the latest GitHub runner environments.

Think of v1.26.3 as the “maintenance and readiness” layer that ensures the high-performance HNSW search you rely on continues to compile and run perfectly on the newest versions of MySQL.

Looking Ahead: The Architecture Pivot (PR #76)

While v1.26.3 keeps us current, the real excitement is happening in the lab.

There is a fundamental architecture change currently in development under Component migration (8.4–9.6) and release workflow update.

Unlike the compatibility fixes in today’s release, PR #76 is a structural overhaul. We are re-engineering how the plugin interacts with the MySQL core. This shift is designed to move MyVector closer to a full Component Architecture, which will eventually offer better lifecycle management and even deeper integration with MySQL’s internal services.

This is a significant pivot in how MyVector is built, and it will set the stage for the next generation of vector performance and observability.

Summary

v1.26.3 is the stable, verified update you need for today’s MySQL 8.4/9.6 environments and tomorrow’s 9.7 upgrade. Meanwhile, work continues on the architectural evolution that will define the future of the project.

Scoped Vector Search with the MyVector Plugin for MySQL — Part III

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From Concepts to Production: Real-World Patterns, Query Plans, and What’s Next

In Part I, we introduced scoped vector search in MySQL using the MyVector plugin, focusing on how semantic similarity and SQL filtering work together.

In Part II, we explored schema design, embedding strategies, HNSW indexing, hybrid queries, and tuning — and closed with a promise to show real-world usage and execution behavior.

This final part completes the series.

Semantic Search with Explicit Scope

In real systems, semantic search is almost never global. Results must be filtered by tenant, user, or domain before ranking by similarity.

SELECT id, title
FROM knowledge_base
WHERE tenant_id = 42
ORDER BY
  myvector_distance(embedding, ?, 'COSINE')
LIMIT 10;

This follows the same pattern introduced earlier in the series:

  • SQL predicates define scope
  • Vector distance defines relevance
  • MySQL remains in control of execution

Real-Time Document Recall (Chunk-Based Retrieval)

Document-level embeddings are often too coarse. Most AI workflows retrieve chunks.

SQL
SELECT chunk_text
FROM document_chunks
WHERE document_id = ?
ORDER BY
  myvector_distance(chunk_embedding, ?, 'L2')
LIMIT 6;

This query pattern is commonly used for:

  • Knowledge-base lookups
  • Assistant context retrieval
  • Pre-RAG recall stages

Chat Message Memory and Re-Ranking

Chronological chat history is rarely useful on its own. Semantic re-ranking allows systems to recall relevant prior messages.

SQL
SELECT message
FROM chat_history
WHERE session_id = ?
ORDER BY
  myvector_distance(message_embedding, ?, 'COSINE')
LIMIT 8;

The result set can be fed directly into an LLM prompt as conversational memory.

Using MyVector in RAG Pipelines

MyVector integrates naturally into Retrieval-Augmented Generation workflows by acting as the retrieval layer.

SQL
SELECT id, content
FROM documents
WHERE MYVECTOR_IS_ANN(
  'mydb.documents.embedding',
  'id',
  ?
)
LIMIT 12;

At this point:

  • Embeddings are generated externally
  • Retrieval happens inside MySQL
  • Generation happens downstream

No additional vector database is required.

Query Execution and Fallback Behavior

ANN Execution Path (HNSW Enabled)

Once an HNSW index is created and loaded, MySQL uses the ANN execution path provided by the plugin.
Candidate IDs are retrieved first, followed by row lookups.

This behavior is visible via EXPLAIN.

Brute-Force Fallback (No HNSW Index)

When no ANN index is available, MyVector falls back to deterministic KNN evaluation.

SQL
SELECT id
FROM documents
ORDER BY
  myvector_distance(embedding, ?, 'L2')
LIMIT 20;

This results in a full scan and sort — slower, but correct and predictable.

Understanding this fallback is critical for production sizing and diagnostics.

Project Update: MyVector v1.26.1

The project continues to move quickly.

MyVector v1.26.1 is now available, introducing enhanced Docker support for:

  • MySQL 8.4 LTS
  • MySQL 9.0

This release significantly improves:

Stop Moving Data — Start Searching It Where It Lives

Across all three parts, the conclusion is consistent:

Vector search does not require a separate database.

With MyVector, you can:

  • Keep data in MySQL
  • Apply strict SQL scoping
  • Use ANN when available
  • Fall back safely when it isn’t

All with observable execution plans and predictable behavior.

Join the Community

Development happens in the open:

Feedback and contributions are welcome.

Next Up: Powering AI-Ready MySQL — When MyVector Meets ProxySQL

The next step is production architecture.

In the next post, we’ll explore:

  • Integrated MCP Server
  • Improved Full Text Search operations
  • Routing vector-heavy queries with ProxySQL
  • Isolating ANN workloads from OLTP traffic
  • Designing AI-ready MySQL deployments that scale safely

MyVector brings semantic search into MySQL.
ProxySQL helps it run at scale.

Stay tuned…

Fiddling with MySQL MCP Server During Holidays

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I was able to get the MySQL MCP Server up and running and it was time to try it first time against a sample data set outside of its test suite.

Let’s start with Stack Overflow and the value of its data for modern LLMs and AI systems. As most of you already know, the questions in forums has significantly dropped since the launch of OpenAI, aka ChatGPT

Source : https://x.com/alojoh/status/2009366134513127462

This is probably true for traditional search engines like Google, although they’re already adopting built-in AI solutions. Other sources, such as Reddit, also provided valuable, quick, and easy information to the masses.

I wanted to spin a subset of StackExchange questions. These are loaded into a local MySQL server. I intended to put the MCP server to a test to find out what’s going on.

Results aren’t only interesting but also very intriguing. The reason is the questions at these forums and their answers. If these questions and answers don’t exist, how will LLMs train themselves to give precise answers? They may otherwise rely on other outdated sources and start hallucinating.

So first thing we spin up the lastes version of the mysql-mcp-server and configure it to work with Claude desktop per the instructions in the documentation. Basically, get the data sample dataset from the StackExchange data dump site. You can ask Cursor, Gemini, or Claude for help. They can set up this data sample in your local MySQL database server. Here’s the github repository for this data set as well.

My prompt: Explore Stack Exchange database

🚁 Stack Exchange: Drones & Model Aircraft – Complete Analysis

📊 Community Overview

Site: Drones and Model Aircraft Stack Exchange
Period: April 2020 – March 2024 (4 years)
Status: 🚨 Declining but quality community

MetricValue
Total Questions908
Total Answers1,277
Total Users5,251
Total Votes9,983
Avg Answers/Question1.41
Acceptance Rate49.45%
Avg Question Views757

I will not copy and paste the rest of the conversation. It’s a big, long one. However, the results are amazing on the subject of Drones.

As a drone pilot for almost a decade, the following questions are seriously intriguing.

🔥 Most Viral Questions (5K+ Views)

TitleViewsScoreAnswers
At what current should I charge a LiPo battery?24,27894
Why do brushless motors have 3 wires?17,83482
What do ESC beeps mean?16,44452
How come DJI Fly App is NOT in Play Store?15,826124
How to configure servo in Betaflight?12,05252

Prompt: Why do multirotors usually have four propellers?

✅ ACCEPTED ANSWER (27 upvotes) – by Kenn Sebesta

TL;DR: Four is the mathematical minimum for simple, robust control

The Physics: 4 Degrees of Freedom

For hovering aircraft, we need to control 4 degrees of freedom (DoF):

  1. Roll (tilt left/right)
  2. Pitch (tilt forward/backward)
  3. Yaw (rotate clockwise/counterclockwise)
  4. Thrust (up/down)

(We can control horizontal movement (x-y) by combining roll/pitch with thrust)

These are some samples. If you run it yourself, there are amazing insights into the data set. Not only that, but you can question the results, trends, and answers.

Going back to MySQL and like any other database out of the box query optimization was terribly wrong. We dug in a bit further. We discovered that most of the queries it ran for the above results were inefficient.

1. Row Count Query – 100x Faster ⚡

Problem: 9 separate full table scans
Solution: Use information_schema.TABLES metadata
Speedup: 2000ms → 20ms

2. Top Users with Posts – 50x Faster ⚡⚡

Problem: Joins ALL posts for ALL users before filtering
Solution: Filter to top 15 users FIRST, then join
Speedup: 800ms → 15ms
✅ Tested successfully – Works perfectly!

3. Monthly Activity – 10x Faster ⚡

Problem: YEAR() and MONTH() functions prevent index usage
Solution: Use DATE_FORMAT() and index-friendly WHERE
Speedup: 500ms → 50ms

4. Vote Percentages – 10x Faster ⚡

Problem: Subquery recalculates total count for each group
Solution: Calculate once with CTE or window function
Speedup: 400ms → 40ms

5. Answer Distribution – 3x Faster ⚡

Problem: CASE expression in GROUP BY
Solution: Group by integer bucket IDs, then label
Speedup: 300ms → 100ms

This led me to create an optimization guide for the Claude agent.

In conclusion, the MCP servers are great resources to explore data sets. With some experimentation and guidance, they can reveal highly valuable analytics use cases. These include marketing and sales data that would normally take too much time and material to cover.

Next up is token usage. If you are also wondering, “Where have all my tokens gone using these AI tools?” I have some thoughts on that topic, too.