Ktx Data Agent Context Layer

ktx Data Agent Context Layer

Skill by ara.so — MCP Skills collection.

ktx is a self-improving context layer that teaches AI agents how to query your data warehouse accurately. It automatically ingests metadata from databases, dbt, LookML, Looker, Metabase, and Notion, builds a semantic layer with approved metric definitions, and exposes everything via CLI and MCP for agent execution.

What ktx Does

• Learns company knowledge: Ingests wiki content, organizes it, removes duplicates, flags contradictions
• Maps the data stack: Samples tables, captures metadata, detects joinable columns, annotates sources
• Builds semantic layer: Combines raw tables and metrics through a join graph that resolves fan/chasm traps
• Serves agents: CLI and MCP tools with semantic search across wiki and semantic-layer entities

Key benefits for agents:

• Query with approved metric definitions instead of inventing SQL every time
• Reuse canonical business logic across questions
• Get context from scattered sources (dbt, Looker, Notion) in one searchable surface

Installation

Global CLI Install

npm install -g @kaelio/ktx

Project-Scoped Install

npm install --save-dev @kaelio/ktx
npx ktx setup

As an MCP Skill

From Claude Code, Codex, Cursor, or OpenCode:

Run npx skills add Kaelio/ktx --skill ktx

Quick Start

# Create or resume a ktx project
ktx setup

# Check project readiness
ktx status

# Build context from configured sources
ktx ingest

# Search semantic layer
ktx sl "revenue"

# Search wiki
ktx wiki "refund policy"

# Start MCP server for agents
ktx mcp start

Project Structure

A ktx project follows this layout:

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)

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

Configuration

ktx.yaml Example

version: 1
project:
  name: analytics
  description: Data warehouse context for product analytics

llm:
  provider: anthropic
  model: claude-sonnet-4-6
  # API key from KTX_ANTHROPIC_API_KEY or .ktx/secrets.yaml

embeddings:
  provider: openai
  model: text-embedding-3-small
  # API key from KTX_OPENAI_API_KEY or .ktx/secrets.yaml

databases:
  warehouse:
    type: postgres
    host: localhost
    port: 5432
    database: analytics
    # Credentials from KTX_DATABASE_WAREHOUSE_USER/PASSWORD env vars

context_sources:
  dbt_main:
    type: dbt
    project_path: ./dbt
    profiles_dir: ~/.dbt
    target: prod
  
  notion_docs:
    type: notion
    # Token from KTX_CONTEXT_SOURCE_NOTION_DOCS_TOKEN env var
    page_ids:
      - a1b2c3d4e5f6
      - f6e5d4c3b2a1

Secrets Management

Store secrets in .ktx/secrets.yaml or environment variables:

# .ktx/secrets.yaml (git-ignored)
llm:
  anthropic_api_key: sk-ant-...

embeddings:
  openai_api_key: sk-...

databases:
  warehouse:
    user: readonly
    password: ...

context_sources:
  notion_docs:
    token: secret_...

Environment variable names follow KTX_<SECTION>_<KEY> pattern:

• KTX_ANTHROPIC_API_KEY
• KTX_OPENAI_API_KEY
• KTX_DATABASE_WAREHOUSE_USER
• KTX_DATABASE_WAREHOUSE_PASSWORD
• KTX_CONTEXT_SOURCE_NOTION_DOCS_TOKEN

Key Commands

Setup & Status

# Interactive setup wizard
ktx setup

# Check project readiness
ktx status

# Example output:
# 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, notion_docs)
# ktx context built: yes
# Agent integration ready: yes (codex:project)

Building Context

# Ingest all configured sources
ktx ingest

# Ingest specific connection
ktx ingest --connection warehouse

# Ingest specific context source
ktx ingest --context-source dbt_main

# Force rebuild (skip cache)
ktx ingest --force

Searching Context

# Search semantic layer for metrics/dimensions
ktx sl "monthly recurring revenue"
ktx sl "user signup date"

# Search wiki pages
ktx wiki "customer refund policy"
ktx wiki "metric calculation rules"

# JSON output for programmatic use
ktx sl "revenue" --json
ktx wiki "refunds" --json

MCP Server

# Start MCP server for agent integration
ktx mcp start

# Start with custom project directory
ktx mcp start --project-dir /path/to/project

# Check MCP server status
ktx mcp status

Semantic Layer Query

# Query using semantic layer (declarative)
ktx query "revenue by month for 2024"

# Raw SQL query (with context hints)
ktx raw-query "SELECT * FROM users WHERE created_at > '2024-01-01'"

Agent Integration Patterns

Claude Code / Codex

After running ktx setup, the MCP server is automatically configured. From your agent:

Use ktx to find the definition of monthly recurring revenue

Search the wiki for our refund policy

Query revenue by product category for Q4 2024 using ktx

Programmatic MCP Client

import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";

const transport = new StdioClientTransport({
  command: "ktx",
  args: ["mcp", "start", "--project-dir", "/path/to/project"],
});

const client = new Client(
  {
    name: "my-agent",
    version: "1.0.0",
  },
  {
    capabilities: {
      tools: {},
    },
  }
);

await client.connect(transport);

// Search semantic layer
const slResults = await client.callTool({
  name: "ktx_sl_search",
  arguments: {
    query: "revenue",
  },
});

// Search wiki
const wikiResults = await client.callTool({
  name: "ktx_wiki_search",
  arguments: {
    query: "refund policy",
  },
});

// Query with semantic layer
const queryResults = await client.callTool({
  name: "ktx_query",
  arguments: {
    query: "revenue by month for 2024",
    connection_id: "warehouse",
  },
});

Semantic Layer YAML

ktx builds semantic sources from ingestion and stores them as YAML:

# semantic-layer/warehouse/users.yaml
type: source
name: users
connection_id: warehouse
table: public.users
description: User account records with signup and subscription data
columns:
  - name: id
    type: integer
    primary_key: true
  - name: email
    type: varchar
    description: User email address
  - name: created_at
    type: timestamp
    description: Account creation timestamp
  - name: plan_id
    type: integer
    foreign_key:
      table: plans
      column: id
dimensions:
  - name: signup_date
    column: created_at
    type: time
    grain: day
measures:
  - name: user_count
    aggregation: count
    description: Total number of users

# semantic-layer/warehouse/mrr.yaml
type: metric
name: monthly_recurring_revenue
connection_id: warehouse
description: Total MRR from active subscriptions
sql: |
  SELECT
    DATE_TRUNC('month', s.start_date) AS month,
    SUM(p.price) AS mrr
  FROM subscriptions s
  JOIN plans p ON s.plan_id = p.id
  WHERE s.status = 'active'
  GROUP BY 1
dimensions:
  - name: month
    type: time
    grain: month
measures:
  - name: mrr
    aggregation: sum
    type: currency

Wiki Pages

ktx organizes wiki content in markdown:

<!-- wiki/global/refund-policy.md -->
---
title: Customer Refund Policy
tags: [policy, customer-service, finance]
---

# Customer Refund Policy

## Eligibility

Customers can request refunds within 30 days of purchase if:
- Product defect
- Service unavailability > 24 hours
- Accidental duplicate purchase

## Processing

Refunds are processed within 5-7 business days.
Finance team approval required for amounts > $500.

## Metric Impact

Refunds reduce `net_revenue` but not `gross_revenue`.
Track via `refund_rate` metric in semantic layer.

Database Connectors

Supported databases:

Example PostgreSQL configuration:

databases:
  warehouse:
    type: postgres
    host: db.example.com
    port: 5432
    database: analytics
    schema: public
    # Credentials from env or secrets.yaml

Example Snowflake configuration:

databases:
  snowflake_prod:
    type: snowflake
    account: xy12345.us-east-1
    warehouse: COMPUTE_WH
    database: ANALYTICS
    schema: PUBLIC
    role: READONLY

Context Source Integrations

dbt

context_sources:
  dbt_main:
    type: dbt
    project_path: ./dbt
    profiles_dir: ~/.dbt
    target: prod

Ingests:

• Model definitions and lineage
• Column descriptions
• Metric definitions (dbt Metrics or MetricFlow)
• Tests and constraints

Looker

context_sources:
  looker_prod:
    type: looker
    api_url: https://looker.example.com:19999
    # Client ID/secret from env vars

LookML

context_sources:
  lookml_repo:
    type: lookml
    repo_path: ./lookml

Metabase

context_sources:
  metabase:
    type: metabase
    url: https://metabase.example.com
    # API key from env var

Notion

context_sources:
  notion_docs:
    type: notion
    page_ids:
      - root-page-id-1
      - root-page-id-2

Common Workflows

Initial Setup for a New Project

# 1. Install ktx
npm install -g @kaelio/ktx

# 2. Navigate to your project
cd ~/analytics-project

# 3. Run interactive setup
ktx setup
# - Select LLM provider (Anthropic/Google/AI Gateway)
# - Configure embeddings provider (OpenAI/Google)
# - Add database connection (Postgres/Snowflake/etc)
# - Add context sources (dbt/Looker/Notion/etc)

# 4. Build initial context
ktx ingest

# 5. Verify setup
ktx status

# 6. Test search
ktx sl "revenue"
ktx wiki "business rules"

Adding a New Database Connection

# Edit ktx.yaml
# Add new database under 'databases:' section

databases:
  new_warehouse:
    type: postgres
    host: new-db.example.com
    port: 5432
    database: prod

# Add credentials to .ktx/secrets.yaml or env vars
export KTX_DATABASE_NEW_WAREHOUSE_USER=readonly
export KTX_DATABASE_NEW_WAREHOUSE_PASSWORD=secret

# Ingest the new connection
ktx ingest --connection new_warehouse

# Verify
ktx status

Updating Context After Schema Changes

# Force rebuild of all context
ktx ingest --force

# Or rebuild specific connection
ktx ingest --connection warehouse --force

# Or rebuild specific context source
ktx ingest --context-source dbt_main --force

Using with Claude Code

# 1. Ensure MCP server is configured
ktx status
# If output shows: "Run ktx mcp start --project-dir ..."
# Copy and run that command before opening Claude Code

# 2. From Claude Code, ask:

Search ktx for the definition of customer lifetime value

Use ktx to query monthly active users for the last 6 months

Check the ktx wiki for our data retention policy

Troubleshooting

"Project ready: no"

ktx status
# Check which component is not ready

# Missing ktx.yaml?
ktx setup

# Missing secrets?
# Add to .ktx/secrets.yaml or export env vars

"LLM ready: no"

# Check API key is set
echo $KTX_ANTHROPIC_API_KEY

# Or add to secrets.yaml
cat > .ktx/secrets.yaml << EOF
llm:
  anthropic_api_key: sk-ant-...
EOF

# Verify provider in ktx.yaml
# llm:
#   provider: anthropic  # or google, ai_gateway

"Databases configured: no"

# Check ktx.yaml has databases section
cat ktx.yaml | grep -A 5 databases

# Test connection credentials
export KTX_DATABASE_WAREHOUSE_USER=readonly
export KTX_DATABASE_WAREHOUSE_PASSWORD=secret

# Try ingest
ktx ingest --connection warehouse

Ingest Fails

# Check connection details
ktx ingest --connection warehouse --verbose

# For dbt sources, verify project path
# context_sources:
#   dbt_main:
#     project_path: ./dbt  # Must contain dbt_project.yml

# For API sources (Looker/Metabase/Notion), verify tokens
echo $KTX_CONTEXT_SOURCE_LOOKER_PROD_CLIENT_ID
echo $KTX_CONTEXT_SOURCE_LOOKER_PROD_CLIENT_SECRET

MCP Server Not Found by Agent

# 1. Check ktx status output for MCP command
ktx status

# 2. Run the command it shows
ktx mcp start --project-dir /path/to/project

# 3. Keep that terminal open while using agent

# For persistent setup, configure agent's MCP settings:
# Claude Code: Add to mcp.json
# Cursor: Add to .cursor/mcp.json

Search Returns No Results

# Ensure context is built
ktx ingest

# Check semantic layer files exist
ls -la semantic-layer/*/

# Check wiki files exist
ls -la wiki/global/

# Try broader search
ktx sl "revenue"  # vs "monthly_recurring_revenue"

# Use JSON output to debug
ktx sl "revenue" --json | jq .

Read-Only Database Error

ktx only reads from your database. If you see write errors:

# Check database user permissions
# User should have SELECT only

# In Postgres:
# GRANT SELECT ON ALL TABLES IN SCHEMA public TO readonly_user;

# In Snowflake:
# GRANT USAGE ON WAREHOUSE COMPUTE_WH TO ROLE READONLY;
# GRANT SELECT ON ALL TABLES IN SCHEMA PUBLIC TO ROLE READONLY;

Telemetry Opt-Out

ktx collects anonymous usage data. To disable:

# Environment variable
export KTX_TELEMETRY_DISABLED=1

# Or in ktx.yaml
telemetry:
  enabled: false

Advanced Usage

Custom Project Directory

# All commands accept --project-dir
ktx status --project-dir /path/to/project
ktx ingest --project-dir /path/to/project
ktx sl "revenue" --project-dir /path/to/project

# Or set environment variable
export KTX_PROJECT_DIR=/path/to/project
ktx status

Multiple Projects

# Project A
cd ~/project-a
ktx setup
ktx ingest

# Project B
cd ~/project-b
ktx setup
ktx ingest

# Query project A from anywhere
ktx sl "revenue" --project-dir ~/project-a

# Query project B from anywhere
ktx wiki "policy" --project-dir ~/project-b

Programmatic Query Execution

import { execSync } from 'child_process';

// Execute ktx command and parse JSON output
function ktxSearch(query: string, type: 'sl' | 'wiki'): any {
  const cmd = type === 'sl' 
    ? `ktx sl "${query}" --json`
    : `ktx wiki "${query}" --json`;
  
  const output = execSync(cmd, { encoding: 'utf-8' });
  return JSON.parse(output);
}

const metrics = ktxSearch('revenue', 'sl');
const policies = ktxSearch('refund', 'wiki');

console.log('Found metrics:', metrics.length);
console.log('Found wiki pages:', policies.length);

Project Resolution

ktx finds your project in this order:

1. --project-dir flag
2. KTX_PROJECT_DIR environment variable
3. Nearest ktx.yaml in current or parent directories
4. Current working directory

Always use --project-dir or KTX_PROJECT_DIR in scripts for reliability.