ktx-context-layer-data-agents

name	ktx-context-layer-data-agents
description	Build and query a context layer for AI data agents with ktx - auto-learning semantic layer, wiki, and MCP integration
triggers	["set up ktx for data agent queries","configure ktx semantic layer","ingest database context with ktx","connect AI agent to warehouse using ktx","search ktx wiki and metrics","configure ktx MCP server","build ktx context from dbt","troubleshoot ktx agent integration"]

ktx Context Layer for Data Agents

Skill by ara.so — MCP Skills collection.

ktx is an executable context layer that teaches AI agents how to query data warehouses accurately. It automatically builds and maintains:

• Semantic layer with approved metric definitions, join graphs, and automatic fan/chasm trap resolution
• Wiki from company knowledge (dbt, Looker, Notion) with deduplication and contradiction detection
• MCP server exposing tools for agent execution via CLI or Model Context Protocol

Agents get one searchable surface instead of reinventing SQL on every prompt.

Installation

# Install globally
npm install -g @kaelio/ktx

# Or use npx
npx @kaelio/ktx --help

Requirements:

• Node.js 18+
• SQL warehouse (PostgreSQL, Snowflake, BigQuery, ClickHouse, MySQL, SQL Server, or SQLite)
• LLM provider API key (Anthropic, Google Vertex AI, or AI Gateway)

Quick Setup

# Interactive setup - creates ktx.yaml, configures providers, builds context
ktx setup

# Check project status
ktx status

# Build context from configured sources
ktx ingest

# Start MCP server for agent integration
ktx mcp start

Example ktx status 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)
ktx context built: yes
Agent integration ready: yes (codex:project)

Project Structure

my-project/
├── ktx.yaml                         # Main configuration
├── semantic-layer/
│   └── warehouse/                   # Per-connection YAML semantic sources
│       ├── customers.yaml
│       └── revenue.yaml
├── wiki/
│   ├── global/                      # Shared business context
│   │   └── refund-policy.md
│   └── user/<user-id>/              # User-scoped notes
├── raw-sources/
│   └── warehouse/                   # Ingest artifacts and reports
└── .ktx/                            # Local state, git-ignored

Commit: ktx.yaml, semantic-layer/, wiki/
Ignore: .ktx/

Configuration (ktx.yaml)

Minimal Configuration

version: 1
project:
  id: analytics-warehouse
  name: Analytics Project

llm:
  provider: anthropic
  model: claude-sonnet-4-6
  apiKeyEnv: ANTHROPIC_API_KEY

embeddings:
  provider: openai
  model: text-embedding-3-small
  apiKeyEnv: OPENAI_API_KEY

databases:
  warehouse:
    type: postgres
    host: db.example.com
    port: 5432
    database: analytics
    user: ktx_reader
    passwordEnv: WAREHOUSE_PASSWORD
    ssl: true

contextSources:
  - id: dbt_main
    type: dbt
    path: ./dbt/target/manifest.json
    database: warehouse

Full Configuration with Multiple Sources

version: 1
project:
  id: multi-source-analytics
  name: Multi-Source Analytics

llm:
  provider: vertex
  model: claude-sonnet-4-6
  region: us-central1
  projectId: my-gcp-project
  credentialsEnv: GOOGLE_APPLICATION_CREDENTIALS

embeddings:
  provider: openai
  model: text-embedding-3-small
  apiKeyEnv: OPENAI_API_KEY

databases:
  warehouse:
    type: snowflake
    account: xy12345.us-east-1
    warehouse: COMPUTE_WH
    database: ANALYTICS
    schema: PUBLIC
    user: KTX_USER
    passwordEnv: SNOWFLAKE_PASSWORD
    role: ANALYST

  clickhouse:
    type: clickhouse
    host: clickhouse.example.com
    port: 8123
    database: events
    user: readonly
     


    
     
     
     
     

    
     
     
     
     

    
     
     
     
     

    
     
     
     
     


   
   
   

Core Commands

Setup and Status

# Create or update project
ktx setup

# Check project health
ktx status

# Validate configuration
ktx config validate

# Show current config
ktx config show

Context Building

# Ingest all configured sources
ktx ingest

# Ingest specific connection
ktx ingest --connection warehouse

# Ingest with verbose output
ktx ingest --verbose

# Force re-ingest (ignores cache)
ktx ingest --force

Searching Context

# Search semantic layer
ktx sl "revenue"
ktx sl "active users" --limit 10

# Search wiki
ktx wiki "refund policy"
ktx wiki "data retention" --connection warehouse

# Describe a specific semantic source
ktx describe semantic customers
ktx describe semantic revenue --connection warehouse

MCP Server

# Start MCP server (required for agent integration)
ktx mcp start

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

# Check if MCP is running
ktx status | grep "Agent integration"

Agent Integration

Claude Code / Codex

From your project directory, tell the agent:

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

Or manually add to claude_desktop_config.json (macOS) or %APPDATA%\Claude\config.json (Windows):

{
  "mcpServers": {
    "ktx": {
      "command": "ktx",
      "args": ["mcp", "start", "--project-dir", "/absolute/path/to/project"]
    }
  }
}

Cursor / OpenCode

Add to MCP settings:

{
  "mcpServers": {
    "ktx": {
      "command": "npx",
      "args": ["@kaelio/ktx", "mcp", "start", "--project-dir", "/absolute/path/to/project"]
    }
  }
}

TypeScript API Usage

import { KtxClient } from '@kaelio/ktx';

// Initialize client
const ktx = new KtxClient({
  projectDir: '/path/to/project',
});

// Search semantic layer
const metrics = await ktx.searchSemanticLayer({
  query: 'revenue',
  limit: 5,
});

console.log(metrics);
// [
//   {
//     id: 'revenue',
//     type: 'metric',
//     sql: 'SUM(amount)',
//     description: 'Total revenue from orders',
//     connection: 'warehouse',
//   }
// ]

// Search wiki
const wikiPages = await ktx.searchWiki({
  query: 'refund policy',
  limit: 3,
});

// Get semantic source details
const customerMetric = await ktx.getSemanticSource({
  connection: 'warehouse',
  sourceId: 'customers',
});

// Execute query with context
const result = await ktx.query({
  connection: ,
  : ,
  : ,
});

Semantic Layer Definition

Creating a Metric (YAML)

semantic-layer/warehouse/revenue.yaml:

type: metric
id: total_revenue
name: Total Revenue
description: Sum of all order amounts excluding refunds
sql: |
  SUM(CASE 
    WHEN status != 'refunded' 
    THEN amount 
    ELSE 0 
  END)
baseTable: orders
tags:
  - finance
  - core
filters:
  - dimension: status
    operator: not_in
    values: ['cancelled', 'fraud']
aggregation: sum

Creating a Dimension

semantic-layer/warehouse/customers.yaml:

type: dimension
id: customer_segment
name: Customer Segment
description: Customer lifecycle segment based on LTV
sql: |
  CASE
    WHEN lifetime_value > 10000 THEN 'enterprise'
    WHEN lifetime_value > 1000 THEN 'mid-market'
    ELSE 'smb'
  END
baseTable: customers
dataType: string
tags:
  - segmentation

Join Definition

type: join
id: orders_to_customers
from: orders
to: customers
relationship: many_to_one
sql: orders.customer_id = customers.id
requiredFilters: []

Wiki Content

Adding Business Context

wiki/global/refund-policy.md:

# Refund Policy

## Definition

A refund is issued when:
- Customer requests within 30 days
- Product is defective
- Service was not delivered

## Metrics Impact

- `refunded_revenue`: Revenue from orders with status = 'refunded'
- `net_revenue`: Total revenue excluding refunds
- `refund_rate`: refunded_revenue / total_revenue

## Related Tables

- `orders.status`: Use 'refunded' for refund detection
- `refunds`: Detailed refund records with reason codes

User-Scoped Notes

wiki/user/[e-mail]/weekly-metrics.md:

# Weekly Metrics Review Notes

## Active Users Definition

Per discussion with Product (2024-05-15):
- Use `events.user_id` not `users.id`
- Filter to `event_type IN ('page_view', 'feature_used')`
- 7-day rolling window

Query stored in: `semantic-layer/warehouse/active_users.yaml`

Common Patterns

Setting Up a New Project

# 1. Initialize project
mkdir analytics-project && cd analytics-project
ktx setup

# 2. Configure in ktx.yaml (edit manually or via setup prompts)

# 3. Add dbt manifest as context source
cat >> ktx.yaml <<EOF
contextSources:
  - id: dbt_main
    type: dbt
    path: ../dbt/target/manifest.json
    database: warehouse
EOF

# 4. Build context
ktx ingest

# 5. Verify
ktx sl "customers"
ktx wiki "refund"

# 6. Start MCP for agents
ktx mcp start

Connecting to Snowflake

databases:
  warehouse:
    type: snowflake
    account: xy12345.us-east-1
    warehouse: COMPUTE_WH
    database: ANALYTICS
    schema: PUBLIC
    user: KTX_USER
    passwordEnv: SNOWFLAKE_PASSWORD
    role: ANALYST
    authenticator: snowflake  # or 'externalbrowser' for SSO

# Set password
export SNOWFLAKE_PASSWORD='your-password'

# Test connection
ktx ingest --connection warehouse

Connecting to BigQuery

databases:
  warehouse:
    type: bigquery
    projectId: my-gcp-project
    dataset: analytics
    credentialsEnv: GOOGLE_APPLICATION_CREDENTIALS
    location: US

# Set credentials path
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json

# Test
ktx ingest --connection warehouse

Using Local LLM via AI Gateway

llm:
  provider: ai-gateway
  baseUrl: http://localhost:8080/v1
  model: llama-3-70b
  apiKeyEnv: AI_GATEWAY_API_KEY  # optional

Ingesting Notion as Wiki Source

contextSources:
  - id: company_wiki
    type: notion
    apiKeyEnv: NOTION_API_KEY
    databaseId: a1b2c3d4e5f6
    enabled: true

export NOTION_API_KEY='secret_...'
ktx ingest

Troubleshooting

"Project ready: no"

# Check what's missing
ktx status

# Common fixes:
# - Missing LLM API key
export ANTHROPIC_API_KEY='sk-ant-...'

# - Missing database password
export WAREHOUSE_PASSWORD='...'

# - Invalid ktx.yaml
ktx config validate

"Agent integration ready: no"

# Check status output for MCP start command
ktx status

# Run the printed command, e.g.:
ktx mcp start --project-dir /home/user/analytics

# Verify MCP config in Claude Desktop:
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%\Claude\config.json

Database Connection Errors

# Test connection explicitly
ktx ingest --connection warehouse --verbose

# Common issues:
# - SSL required: Add ssl: true to database config
# - Firewall: Verify host/port accessibility
# - Credentials: Check passwordEnv variable is set
# - Permissions: Ensure user has SELECT grants on target schema

Ingestion Fails with "No tables found"

# Check database config schema/dataset
cat ktx.yaml | grep -A 10 databases

# For Snowflake: Verify warehouse, database, schema, role
# For BigQuery: Verify projectId, dataset
# For Postgres: Verify database, schema (defaults to 'public')

# List accessible tables manually:
# PostgreSQL: SELECT tablename FROM pg_tables WHERE schemaname = 'public';
# Snowflake: SHOW TABLES IN SCHEMA analytics.public;

Semantic Layer Not Resolving Joins

# Check join definitions
ls semantic-layer/warehouse/

# Ensure joins are defined:
# type: join
# relationship: many_to_one | one_to_many | one_to_one

# Run with verbose logging
ktx ingest --verbose

# Check for fan trap warnings in output

MCP Tools Not Available in Agent

# 1. Ensure MCP server is running
ktx mcp start --project-dir /absolute/path

# 2. Restart agent client completely (not just reload)

# 3. Check MCP server logs
# Look for connection attempts and errors

# 4. Verify absolute path in MCP config (no ~, no relative paths)

Slow Ingestion

# Reduce table sample size
# In ktx.yaml:
ingestion:
  tableSampleSize: 100  # default 1000
  enableJoinDiscovery: false  # if not needed
  enableColumnProfiling: false  # if not needed

# Ingest specific connection only
ktx ingest --connection warehouse

Contradictions Flagged During Ingest

# Review flagged contradictions in raw-sources/<connection>/reports/
cat raw-sources/warehouse/reports/contradictions.json

# Example:
# {
#   "metric": "active_users",
#   "sources": ["dbt", "looker"],
#   "conflict": "Different SQL definitions"
# }

# Resolve by:
# 1. Choose canonical source
# 2. Update semantic-layer/<connection>/<metric>.yaml
# 3. Document decision in wiki/global/<metric>-definition.md

Type Errors with TypeScript API

// Ensure @kaelio/ktx is installed as dependency, not devDependency
// npm install @kaelio/ktx

// Import types explicitly
import type { SemanticSource, WikiPage } from '@kaelio/ktx';

const metric: SemanticSource = await ktx.getSemanticSource({
  connection: 'warehouse',
  sourceId: 'revenue',
});

Advanced Usage

Programmatic Project Setup

import { KtxProject } from '@kaelio/ktx';

const project = await KtxProject.create({
  projectDir: '/path/to/project',
  config: {
    version: 1,
    project: {
      id: 'analytics',
      name: 'Analytics Project',
    },
    llm: {
      provider: 'anthropic',
      model: 'claude-sonnet-4-6',
      apiKeyEnv: 'ANTHROPIC_API_KEY',
    },
    embeddings: {
      provider: 'openai',
      model: 'text-embedding-3-small',
      apiKeyEnv: 'OPENAI_API_KEY',
    },
    databases: {
      warehouse: {
        type: 'postgres',
        host: 'localhost',
        port: 5432,
        database: 'analytics',
        user: 'ktx',
        passwordEnv: 'DB_PASSWORD',
      },
    },
    contextSources: [],
  },
});

await project.ingest();

Custom Embedding Search

const results = await ktx.searchSemanticLayer({
  query: 'monthly recurring revenue',
  limit: 5,
  threshold: 0.7,  // Similarity threshold (0-1)
  filters: {
    tags: ['finance'],
    connection: 'warehouse',
  },
});

Exporting Context for External Use

# Export semantic layer as JSON
ktx export semantic --connection warehouse > semantic-layer.json

# Export wiki as markdown archive
ktx export wiki > wiki-export.tar.gz

Resources

• Documentation: https://docs.kaelio.com/ktx/docs/
• CLI Reference: https://docs.kaelio.com/ktx/docs/cli-reference/ktx
• Slack Community: https://join.slack.com/t/ktxcommunity/shared_invite/zt-3y9b44m1x-LVyNNJD5nwaZHq4XS29LMQ
• GitHub Issues: https://github.com/Kaelio/ktx/issues
• Agent Quickstart: https://docs.kaelio.com/ktx/docs/ai-resources/agent-quickstart