IMTI

fewbottles.com is a working cocktail app that costs me a domain name and nothing else.

A Chief Engineer with over twenty-five years of experience in software architecture, development, and digital transformation.

I have been assembling data platforms for clients for over twenty years.

An analyst logs in, Claude calls trino_query, and Trino has to authenticate as someone.

If you operate the MCP server, you pre-register the clients.

A REST API with thousands of endpoints will generate thousands of functions for an agent if you run the usual OpenAPI generator.

This is MCP by Design, an ongoing development log of mcp-data-platform, a Go server I run in front of a warehouse, a catalog, object storage, APIs, other MCP servers, and a knowledge base, also hosted as Plexara.

A fifty-megabyte CSV from a vendor is sitting in a bucket, or about to be.

Agents write good reports and dashboards.

Function-level lint cannot see a god package. gocyclo at 10, funlen at 80, revive’s argument limits: all of those look inside one function.

I work from Los Angeles, at Deasil Works.

mcp-data-platform exposes knowledge as wiki-style pages.

Agents on mcp-data-platform write notes during a session.

The clearest reason to put your own hardware in the hybrid cluster is the GPU.

The platform so far runs on a set of cloud virtual machines, and that is the right default.

The 2020 book had a chapter on Blockchain.

Some work on a platform is a small piece of logic that should run on demand and cost nothing when idle: enrich a record, transform a payload, call out to a model, respond to an event.

Postgres is the platform’s default database, and most of the time it is the right answer.

A common cloud pattern is a file landing in a bucket that automatically kicks off some work: a CSV arrives and gets validated, an image gets a thumbnail, a log file gets compressed.

The two benchmark reports I published in July, the knowledge-layer ablation and the stored-knowledge study, both came out of the same piece of software: a benchmark harness in the bench/ directory of mcp-data-platform, roughly 36,000 lines of Go in its own

The platform stores, moves, and serves a lot of data, and the people working with it need a place to write Python, run a query, build a model, and explore.

A data platform that takes in device and sensor data needs a way to receive it that suits how those devices actually work: small, intermittent, often on flaky networks, sometimes a microcontroller with kilobytes of memory.

Every component in this series has been exposing metrics: Postgres, Kafka, OpenSearch, Cilium, the cluster itself, each one had a line about the monitoring stack scraping it “later.”

Agents running on mcp-data-platform do something the design never asked for: they store world state as knowledge.

When you built the cluster, one cilium install gave it pod networking, replaced kube-proxy, and encrypted traffic between nodes, three jobs the 2020 edition did with three separate pieces.

When you built the cluster, the container runtime under the kubelet was containerd, with Docker nowhere in sight.

Give a frontier model raw SQL access to a warehouse and ask it for last quarter’s top account by revenue.

The 2020 book covered building software on the platform, and it stopped there.

The Forgejo pipelines from the last post build container images.

A platform needs a place to keep its code, run its pipelines, and store the images it builds.

A frontier model can run SQL against everything you have built.

The platform now holds data in a lot of places: tables in Postgres, topics in Kafka, indices in OpenSearch, and Iceberg tables in the lakehouse.

Everything built so far stores, moves, and queries data.

A platform spends most of its life moving data between systems: pulling from an API, reshaping a payload, draining a queue into a table, fanning one feed out to three destinations.

The platform is about to grow tools that people log into: a dataflow canvas, a BI front end, dashboards.

A platform’s analytical questions, the roll-ups, joins, and slices over years of data, want a warehouse.

A data platform needs object storage: the S3-compatible bucket layer that holds a data lake’s raw files, the destination for database and stream backups, and the storage under the lakehouse tables coming next.

Some questions are slow on a relational database no matter how you index them.

When a platform needs to move events in real time, many producers, many consumers, durable and replayable, Kafka is the backbone.

I gave an agent one brief and pointed it at my own platform: connect as a real MCP client, exercise every tool, and find every place where what the tool says and what the tool does disagree.

Every platform needs a database, and for most of what you build it should be PostgreSQL.

Halfway through a query session an analyst types a sentence that is worth more than the query: “timestamps before March 2024 are UTC, after that they are America/Chicago.”

The 2020 edition exposed services to the outside world with the Nginx Ingress controller, a daemon on every node terminating ports 80 and 443.

Agents skip the steps you want them to take.

The cluster from the last post can schedule containers, but it cannot yet keep anything.

The standard way to give an agent access to three systems is to run three MCP servers.

Every platform in this series sits on one foundation: a Kubernetes cluster you own outright.

An MCP server is written by an agent, used by an agent, and now often evaluated by an agent, and every one of them can confabulate.

Ninety percent of what I search for now lands me on an article that was clearly generated, never read by a human, and written from nothing.

In 2020, Apress published my book, Advanced Platform Development with Kubernetes.