Enables AI agents to search and discover data across SQLite and CSV sources through an MCP interface, with metadata indexing and fuzzy search capabilities.
Exposes schema, lineage, and data-quality trust signals from a SQLite-backed catalog as MCP tools, enabling AI agents to answer grounded questions about datasets without hallucinating.
Enables natural language querying of SQL databases by allowing the agent to explore schema, write SQL, and self-correct errors. It integrates with MCP-compatible assistants like Claude Desktop or Cursor.
Enables AI agents to query a SQLite database using natural language through the Model Context Protocol (MCP). Includes security guardrails that block destructive SQL operations.
Enables natural language data analysis on uploaded CSV files by converting them to SQLite and exposing read-only database tools via MCP. Integrates with Ollama LLM to translate user questions into safe SQL queries.