catalyst-builder 1.1.0

Build AI-powered integrations for any business system in minutes

Catalyst Builder

Tools for creating and validating Catalyst Knowledge Packs.

Installation

pip install catalyst-builder

What are Knowledge Packs?

Knowledge Packs are YAML configurations that define tools for integrating with external systems through the MCP (Model Context Protocol).

What's New in v1.1.0

LLM-Optimized Knowledge Packs - New optional features to improve AI tool discovery and usage:

• Smart Tool Metadata - Display names, usage hints, complexity levels
• Parameter Constraints - Min/max values, examples, validation patterns
• Tool Prerequisites - Define safe tool usage sequences
• External Transforms - Reference Python/JS files for better maintainability

All features are 100% backward compatible - existing packs continue to work unchanged!

See the LLM Optimization Guide for details.

Pack Structure

# pack.yaml
metadata:
  name: "my_integration" 
  version: "1.0.0"
  description: "Integration with external API"

connection:
  type: "rest"
  base_url: "${API_URL}"
  auth:
    method: "bearer"
    token: "${API_TOKEN}"

tools:
  list_items:
    type: "list"
    description: "Get list of items"
    endpoint: "/items"
    method: "GET"

Supported Integration Types

• REST API - HTTP/HTTPS API integrations
• Database - SQL and NoSQL database connections
• File System - Local files, S3, Azure Blob, Google Cloud Storage
• SSH - Remote system access
• Message Queue - RabbitMQ, Kafka, Redis Pub/Sub

Tool Types

• list - Get arrays of data
• details - Get specific resource details
• query - Run database queries
• search - Search with parameters
• execute - Run commands or scripts

Parameters

Define parameters for dynamic tools:

tools:
  search_users:
    type: "query"
    sql: "SELECT * FROM users WHERE created_at > {since_date}"
    parameters:
      - name: "since_date"
        type: "string"
        required: true

Data Transformation

Transform responses with jq, Python, JavaScript, or templates:

tools:
  process_data:
    type: "query"
    sql: "SELECT id, name, status FROM users"
    transform:
      type: "jq"
      expression: '.[] | {id, name, active: .status == "active"}'

Validation

python -c "from catalyst_pack_schemas.validator import PackValidator; print(PackValidator().validate_pack('path/to/pack'))"

Environment Variables

Use environment variables for sensitive data:

connection:
  host: "${DB_HOST}"
  auth:
    username: "${DB_USER}"
    password: "${DB_PASSWORD}"

Dependencies

Optional dependencies for specific integrations:

# Database connections
pip install asyncpg          # PostgreSQL
pip install aiomysql         # MySQL
pip install aiosqlite        # SQLite
pip install motor            # MongoDB
pip install redis            # Redis

# Cloud storage
pip install aioboto3         # AWS S3
pip install google-cloud-storage  # Google Cloud
pip install azure-storage-blob    # Azure Blob

# Other integrations
pip install aio-pika         # RabbitMQ
pip install aiokafka         # Apache Kafka
pip install asyncssh         # SSH connections

Documentation

• Integration Types - Detailed integration patterns
• Pack Structure - Pack organization guide
• Pack Development - Creation guide
• Security - Security patterns

Examples

See examples/ directory for sample packs demonstrating various integration patterns.