Introducing Thinking Prompt

Three Tabs, One Workspace

Thinking Prompt is built around three integrated tabs that cover the full lifecycle of data engineering work:

• Terminal — Execute SSH commands on remote servers, inspect infrastructure, manage deployments
• AI Chat — Converse with AI agents that have real tool access to your databases, servers, and MCP tools
• Codegen — Manage AI-generated PySpark and SQL scripts, view execution logs, re-run jobs with one click

AI Agent with Real Tool Calling

Unlike simple AI assistants that only generate text, Thinking Prompt agents operate through a tool-calling loop — the LLM decides which tools to invoke, receives real results, reasons about them, and calls more tools until the task is complete.

• Database Tools — List connections, discover schemas, query tables, batch multi-table fetches
• SSH Tools — Execute commands on remote servers, read files from remote hosts
• MCP Tools — Connect any Model Context Protocol server for extensible tool access
• Code Generation — Generate, save, and execute SQL and PySpark scripts directly

Multi-Model BYOK

Thinking Prompt is model-agnostic. Connect your preferred LLM provider — or let different users choose different models. Each user configures their own API key, model, and context window size.

Model Context Protocol (MCP)

Thinking Prompt includes a native MCP client, so the agent can connect to any MCP-compatible server and use its tools as if they were built-in. Configure connections via stdio or HTTP transport, mark them as auto-connect, and the agent's system prompt is automatically enriched with available tools.

• Connect a Postgres MCP server for advanced database operations
• Add a GitHub MCP server for repository management
• Plug in custom MCP servers for proprietary data sources
• Chain multiple MCP servers for cross-system workflows

PySpark Execution Engine

A production-grade PySpark runtime that runs AI-generated scripts in isolated subprocesses. The runtime auto-generates a wrapper that initializes SparkSession, injects credentials (auto-deleted after read), and provides helper functions like read_sql(), write_table(), and execute_sql().

• Auto-Dependency Install — Missing packages detected and installed before execution
• Real-time Log Streaming — stdout/stderr streamed to browser via WebSocket
• Credential Isolation — Credentials written to temp file, deleted immediately after read
• Cluster Support — Local, Spark standalone, YARN, Kubernetes

Cross-Database ETL

The agent builds execution plans that span multiple databases in a single operation. Read from PostgreSQL, transform with PySpark, write to MySQL, and create reporting views on Snowflake — all orchestrated autonomously.

Smart Intent Routing

Every user message is classified by the LLM into one of three execution paths, ensuring the right strategy for every request: