# Pylar

## Docs

- [A/B Testing Assistant](https://docs.pylar.ai/examples/ab-testing-assistant.md): Build an AI agent that analyzes experiments, determines statistical significance, and recommends winners
- [Content Performance Analyzer](https://docs.pylar.ai/examples/content-performance-analyzer.md): Build an AI agent that analyzes blog posts, content engagement, content ROI, and identifies top performers
- [Customer Churn Predictor](https://docs.pylar.ai/examples/customer-churn-predictor.md): Build an AI agent that identifies at-risk customers and recommends retention actions using usage and engagement data
- [Customer Onboarding Assistant](https://docs.pylar.ai/examples/customer-onboarding-assistant.md): Build an AI agent that tracks onboarding progress, identifies blockers, and provides personalized guidance
- [Customer Support Agent](https://docs.pylar.ai/examples/customer-support-agent.md): Build an AI agent that handles support tickets with full customer context from multiple data sources
- [Customer Wiki Agent](https://docs.pylar.ai/examples/customer-wiki-agent.md): Build an AI agent that creates and maintains a customer knowledge base from support interactions and product data
- [Expense Auditor](https://docs.pylar.ai/examples/expense-auditor.md): Build an AI agent that reviews expenses, identifies anomalies, ensures compliance, and generates audit reports
- [Feature Flag Manager](https://docs.pylar.ai/examples/feature-flag-manager.md): Build an AI agent that manages feature rollouts, analyzes flag performance, and recommends rollback decisions
- [Financial Analyst](https://docs.pylar.ai/examples/financial-analyst.md): Build an AI agent that analyzes revenue trends, expenses, profitability, and generates financial reports
- [Invoice & Billing Assistant](https://docs.pylar.ai/examples/invoice-billing-assistant.md): Build an AI agent that handles billing queries, generates invoices, tracks payments, and manages collections
- [IT Operations Monitor](https://docs.pylar.ai/examples/it-operations-monitor.md): Build an AI agent that monitors system health, analyzes performance metrics, and generates incident reports
- [Lead Qualification Agent](https://docs.pylar.ai/examples/lead-qualification-agent.md): Build an AI agent that scores leads, routes to appropriate sales reps, and prioritizes high-value prospects
- [Marketing Attribution Analyzer](https://docs.pylar.ai/examples/marketing-attribution-analyzer.md): Build an AI agent that tracks customer journey, attributes conversions, and measures channel effectiveness
- [Marketing Campaign Optimizer](https://docs.pylar.ai/examples/marketing-campaign-optimizer.md): Build an AI agent that analyzes campaign performance, identifies high-value segments, and optimizes marketing spend
- [Product Feedback Analyzer](https://docs.pylar.ai/examples/product-feedback-analyzer.md): Build an AI agent that analyzes user feedback, feature requests, sentiment, and prioritizes improvements
- [Product Usage Analyst](https://docs.pylar.ai/examples/product-usage-analyst.md): Build an AI agent that analyzes product usage, feature adoption, user engagement, and identifies trends
- [Revenue Operations Agent](https://docs.pylar.ai/examples/revenue-operations-agent.md): Build an AI agent for revenue forecasting, deal analysis, pipeline health monitoring, and quota tracking
- [Sales Assistant](https://docs.pylar.ai/examples/sales-assistant.md): Build an AI agent that analyzes sales pipeline, identifies opportunities, forecasts revenue, and tracks deal progress
- [Sales Territory Optimizer](https://docs.pylar.ai/examples/sales-territory-optimizer.md): Build an AI agent that allocates territories, balances workload, and identifies growth opportunities
- [Supply Chain Coordinator](https://docs.pylar.ai/examples/supply-chain-coordinator.md): Build an AI agent that manages inventory levels, tracks shipments, and optimizes reorder points
- [Frequently Asked Questions](https://docs.pylar.ai/help/faq.md): Find answers to common questions about Pylar, connections, views, MCP tools, and more
- [Troubleshooting Guide](https://docs.pylar.ai/help/troubleshooting.md): Common issues and solutions for Pylar connections, views, tools, and agents
- [Quick Start](https://docs.pylar.ai/introduction/quick-start.md): Get started with Pylar in minutes - connect databases, create views, build MCP tools, and deploy to your AI agents
- [What is Pylar?](https://docs.pylar.ai/introduction/what-is-pylar.md): Learn about Pylar, a secure data access layer designed for AI agents to safely interact with structured data sources
- [Why Pylar?](https://docs.pylar.ai/introduction/why-pylar.md): Discover the benefits and advantages of using Pylar for secure AI agent data access
- [Creating Tools with AI](https://docs.pylar.ai/learn/building-mcp-tools/creating-tools-with-ai.md): Learn how to use Pylar's AI to create MCP tools from natural language prompts
- [Editing MCP Tools](https://docs.pylar.ai/learn/building-mcp-tools/editing-mcp-tools.md): Learn how to view, edit, and refine MCP tools in Pylar
- [Overview](https://docs.pylar.ai/learn/building-mcp-tools/overview.md): Introduction to MCP tools in Pylar - understand how tools enable AI agents to interact with your data views
- [Testing Your Tools](https://docs.pylar.ai/learn/building-mcp-tools/testing-your-tools.md): Learn how to test MCP tools before publishing to ensure they work correctly with your data
- [Understanding Tool Structure](https://docs.pylar.ai/learn/building-mcp-tools/understanding-tool-structure.md): Learn about the components of an MCP tool: function name, description, SQL query, parameters, and more
- [Claude Desktop](https://docs.pylar.ai/learn/connecting-agent-builders/claude-desktop.md): Connect Pylar MCP tools to Claude Desktop for AI-powered data access
- [Cursor](https://docs.pylar.ai/learn/connecting-agent-builders/cursor.md): Connect Pylar MCP tools to Cursor for AI-powered coding assistance with data access
- [LangGraph](https://docs.pylar.ai/learn/connecting-agent-builders/langgraph.md): Connect Pylar MCP tools to LangGraph for building stateful agent workflows with data access
- [Make](https://docs.pylar.ai/learn/connecting-agent-builders/make.md): Connect Pylar MCP tools to Make (Integromat) for visual automation workflows with data access
- [n8n](https://docs.pylar.ai/learn/connecting-agent-builders/n8n.md): Connect Pylar MCP tools to n8n for workflow automation with visual data access
- [OpenAI Platform](https://docs.pylar.ai/learn/connecting-agent-builders/openai-platform.md): Connect Pylar MCP tools to OpenAI's platform for AI agent development
- [Overview](https://docs.pylar.ai/learn/connecting-agent-builders/overview.md): Connect any agent builder to Pylar - code-driven or no-code, all controlled from one unified platform
- [VS Code](https://docs.pylar.ai/learn/connecting-agent-builders/vs-code.md): Connect Pylar MCP tools to VS Code using Python scripts for programmatic data access
- [Windsurf](https://docs.pylar.ai/learn/connecting-agent-builders/windsurf.md): Connect Pylar MCP tools to Windsurf for AI-powered coding assistance with data access
- [Zapier](https://docs.pylar.ai/learn/connecting-agent-builders/zapier.md): Connect Pylar MCP tools to Zapier for no-code automation workflows with data access
- [Cross-Database Joins](https://docs.pylar.ai/learn/creating-data-views/cross-database-joins.md): Learn how to query and join data across multiple databases and dataframes in Pylar to create unified views
- [Overview](https://docs.pylar.ai/learn/creating-data-views/overview.md): Introduction to data views in Pylar - understand how views provide secure, governed access to your data
- [SQL IDE Basics](https://docs.pylar.ai/learn/creating-data-views/sql-ide-basics.md): Learn how to use Pylar's built-in SQL IDE to query data sources, work with dataframes, and create views
- [Writing Your First View](https://docs.pylar.ai/learn/creating-data-views/writing-your-first-view.md): Step-by-step guide to creating your first data view in Pylar, including cross-database joins from Snowflake, HubSpot, and Salesforce
- [Analyzing Errors](https://docs.pylar.ai/learn/evals/analyzing-errors.md): Learn how to identify, understand, and fix errors in your MCP tools using Evals
- [Evals Dashboard](https://docs.pylar.ai/learn/evals/evals-dashboard.md): Navigate the Evals dashboard and understand evaluation metrics, visual insights, and filters
- [Overview](https://docs.pylar.ai/learn/evals/overview.md): Introduction to Evals in Pylar - monitor and analyze how AI agents interact with your MCP tools
- [Understanding Query Logs](https://docs.pylar.ai/learn/evals/understanding-query-logs.md): Learn how to read and analyze raw query logs from Evals to understand tool execution details
- [Understanding Query Shapes](https://docs.pylar.ai/learn/evals/understanding-query-shapes.md): Learn how to analyze query patterns and shapes from Evals to understand how agents use your tools
- [Connecting BigQuery](https://docs.pylar.ai/learn/making-connections/connecting-bigquery.md): Step-by-step guide to connecting your Google Cloud BigQuery data warehouse to Pylar
- [Connecting MotherDuck](https://docs.pylar.ai/learn/making-connections/connecting-motherduck.md): Step-by-step guide to connecting your MotherDuck serverless analytics platform to Pylar
- [Connecting MySQL](https://docs.pylar.ai/learn/making-connections/connecting-mysql.md): Step-by-step guide to connecting your MySQL database to Pylar
- [Connecting PostgreSQL](https://docs.pylar.ai/learn/making-connections/connecting-postgresql.md): Step-by-step guide to connecting your PostgreSQL database to Pylar
- [Connecting Redshift](https://docs.pylar.ai/learn/making-connections/connecting-redshift.md): Step-by-step guide to connecting your Amazon Redshift data warehouse to Pylar
- [Connecting Snowflake](https://docs.pylar.ai/learn/making-connections/connecting-snowflake.md): Step-by-step guide to connecting your Snowflake data warehouse to Pylar
- [Connecting Supabase](https://docs.pylar.ai/learn/making-connections/connecting-supabase.md): Step-by-step guide to connecting your Supabase (hosted PostgreSQL) database to Pylar
- [Connecting via SSH](https://docs.pylar.ai/learn/making-connections/connecting-via-ssh.md): Enhanced security option: Connect databases to Pylar using SSH tunnels for maximum data privacy
- [Connection Security](https://docs.pylar.ai/learn/making-connections/connection-security.md): Learn about data security in Pylar and best practices for securing your database connections
- [Managing Connections](https://docs.pylar.ai/learn/making-connections/managing-connections.md): Learn how to view, edit, test, and manage your database connections in Pylar
- [Overview](https://docs.pylar.ai/learn/making-connections/overview.md): Introduction to connecting data sources to Pylar - learn about connection types and the connection process
- [Supported Data Sources](https://docs.pylar.ai/learn/making-connections/supported-data-sources.md): Complete list of data sources supported by Pylar, including databases, data warehouses, and business applications
- [Troubleshooting Connections](https://docs.pylar.ai/learn/making-connections/troubleshooting-connections.md): Common connection issues and solutions for connecting data sources to Pylar
- [Overview](https://docs.pylar.ai/learn/publishing-tools/overview.md): Introduction to publishing MCP tools - make your tools available to AI agents through secure connection details
- [Publishing Your Tools](https://docs.pylar.ai/learn/publishing-tools/publishing-your-tools.md): Step-by-step guide to publishing your MCP tools and generating secure access credentials

## OpenAPI Specs

- [openapi](https://docs.pylar.ai/api-reference/openapi.json)