raw/ktx-ai-data-agents-mcp-context-skills.md

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url: https://github.com/Kaelio/ktx-ai-data-agents-mcp-context-skills date_fetched: 2026-07-05 backfilled: true

Quickstart · CLI Reference · Agent Setup · Slack

Built and maintained by Kaelio

ktx is a self-improving context layer that teaches agents how to query your warehouse accurately - from approved metric definitions, joinable columns, and business knowledge it builds and maintains for you.

Note

Run ktx with your own LLM API keys or a local agent sign-in — a Claude Pro/Max subscription through Claude Code, or your local Codex authentication. No extra usage billing from ktx.

General-purpose agents struggle on data tasks. They re-explore your warehouse on every question, invent their own metric logic, and return numbers that don't match approved definitions.

Traditional semantic layers don't fix this. They demand constant manual upkeep and don't absorb the rest of your company's knowledge.

ktx does both, automatically:

Learns from company knowledge.Ingests wiki content, organizes it, removes duplicates, and flags contradictions for human review.
Maps the data stack.Samples tables, captures metadata and usage patterns, detects joinable columns, and annotates sources so agents write better queries.
Builds a semantic layer.Combines raw tables and high-level metrics through a join graph that automatically resolves chasm and fan traps, so agents fetch metrics declaratively instead of rewriting canonical SQL each time.
Serves agents at execution.Exposes CLI and MCP tools with combined full-text and semantic search across wiki and semantic-layer entities.

General-purpose agent	Traditional semantic layer	ktx
Builds warehouse context automatically	—	—	✓
Detects joinable columns + resolves fan/chasm traps	—	Manual	✓
Approved, reusable metric definitions	—	✓	✓
Absorbs wiki / Notion / team knowledge	—	—	✓
Flags contradictions across sources	—	—	✓
Ships CLI + MCP for agent execution	Partial	—	✓
Read-only by design	n/a	n/a	✓

Use ktx if you:

Want agents like Claude Code, Codex, Cursor, or OpenCode to query your warehouse with approved metric definitions
Have business knowledge scattered across dbt, Looker, Metabase, Notion, and team wikis
Need agents to reuse canonical SQL instead of inventing it on every prompt

Skip ktx if you:

You don't have a SQL warehouse - ktxsits on top of one
You only need one ad-hoc query - psqlor a notebook will do

Works with PostgreSQL, Snowflake, BigQuery, ClickHouse, MySQL, SQL Server, SQLite, DuckDB, Amazon Athena, and MongoDB. Integrates with dbt, MetricFlow, LookML, Looker, Metabase, Sigma, Notion, and Google Drive.

npm install -g @kaelio/ktx
ktx setup
ktx status

ktx setup creates or resumes a local ktx project, configures providers and connections, builds context, and installs agent integration.

Example ktx status after setup:

ktx project: /home/user/analytics
Project ready: yes
LLM ready: yes (claude-sonnet-4-6)
Embeddings ready: yes (text-embedding-3-small)
Databases configured: yes (warehouse)
Context sources configured: yes (dbt_main)
ktx context built: yes
Agent integration ready: yes (codex:project)

Tip

Already using an agent? Ask Claude Code, Codex, Cursor, or OpenCode from your project directory:

Run npx skills add Kaelio/ktx --skill ktx and use the ktx skill to install
and configure ktx in this project.

Important

If ktx status prints ktx mcp start --project-dir ..., run it before opening your agent client.

Re-run the global install with the @latest tag:

npm install -g @kaelio/ktx@latest| Command | Purpose | |---|---| | ktx setup | Create, resume, or update a ktxproject | | ktx status | Check project readiness | | ktx ingest | Build context for every configured connection | | ktx sl "revenue" | Search semantic sources | | ktx wiki "refund policy" | Search local wiki pages | | ktx mcp start | Start the MCP server for agent clients |

See the CLI Reference for every command, flag, and option.

my-project/
├── ktx.yaml                         # Project configuration
├── semantic-layer/<connection-id>/  # YAML semantic sources
├── wiki/global/                     # Shared business context
├── wiki/user/<user-id>/             # User-scoped notes
├── raw-sources/<connection-id>/     # Ingest artifacts and reports
└── .ktx/                            # Local state and secrets, git-ignored

Commit ktx.yaml, semantic-layer/, and wiki/. Keep .ktx/ local.

Project resolution defaults to KTX_PROJECT_DIR, then the nearest ktx.yaml, then the current directory. Pass --project-dir <path> when scripting.

Does ktx send my schema or query results to a hosted service?No.- ktxruns locally. The only data leaving your machine is what you send to the LLM provider you configured.
Which LLM backends are supported?Anthropic API, Google Vertex AI, AI Gateway, the local Claude Code session through the Claude Agent SDK, and your local Codex authentication through the Codex SDK. See LLM configuration.
How is ktx different from a dbt or MetricFlow semantic layer?- ktx- ingeststhose layers and combines them with raw-table introspection and wiki content. Agents get one searchable surface instead of three disconnected ones - and- ktxflags contradictions across sources.
Does ktx need a running server?There is no hosted service. The local MCP daemon runs on demand via- ktx mcp startwhen an agent client needs it.
Is my warehouse safe?Yes. Connections are read-only -- ktxnever writes to your database.
Slack— ask questions, share what you're building, and chat with maintainers.
GitHub Issues— report bugs and request features.
Contributing— set up the repo, run tests, and open a PR.

git clone https://github.com/kaelio/ktx.git
cd ktx
pnpm install
uv sync --all-groups
pnpm run build
pnpm run check

ktx is a pnpm + uv workspace:

Path	Purpose
`packages/cli`	TypeScript CLI and published npm package source
`packages/cli/src/context`	Core context engine
`packages/cli/src/llm`	LLM and embedding providers
`packages/cli/src/connectors`	Database scan connectors
`python/ktx-sl`	Semantic-layer query planning
`python/ktx-daemon`	Portable compute service

Local development CLI:

pnpm run setup:dev
pnpm run link:dev
ktx-dev --help

Useful checks:

pnpm run type-check
pnpm run test
pnpm run dead-code
uv run pytest -q

ktx collects privacy-conscious usage telemetry to understand installs and improve setup, command reliability, and data-agent workflows. Catalog telemetry events do not record file paths, hostnames, SQL, schema names, table names, column names, error messages, raw environment values, or argv. Error reports use PostHog Error Tracking and can include stack frames and raw error messages, which may contain local file paths or the local username in those paths. ktx redacts secrets, credentials, database URLs, auth headers, argv, raw environment values, SQL text, row data, and user-typed prompt or MCP argument text from the explicit $exception payload. See Telemetry for the event catalog and opt-out options.

ktx is licensed under the Apache License, Version 2.0. See LICENSE.