An MCP server that computes Canadian adjusted cost base (ACB) and capital gains from trade history, including average-cost tracking and superficial-loss detection, returning structured JSON.
Enables conversational analysis of CSV and Parquet files through natural language, providing statistics, summaries, data type information, and comprehensive multi-step data analysis.
Enables language models to run data-quality checks and profiling on local files, using dbt-style assertions like not_null, unique, relationships, and accepted_values.
Enables data analysis on CSV/Excel files using pandas. Supports profiling, column interpretation, sandboxed code execution, and interactive chart generation.
MCP server that profiles local data files (CSV, Parquet, JSON, Excel) and returns compact structured summaries with data-quality flags, enabling AI agents to understand datasets without seeing raw rows.
Enables LLM agents to load, explore, and analyze CSV and Excel files using DuckDB, with tools for SQL querying, statistical analysis, expense optimization, and anomaly detection.