Labsco
MCP SERVER

Pipetable

by melihbirim ↗

Point an assistant at a folder of CSV, Parquet or JSON files and let it query them with real DuckDB SQL instead of guessing.

Spreadsheet & Tabular File Analysis
Summary
The answer comes from DuckDB, not from the model.

That is the whole argument, and it is the right one for tabular data: a model asked to summarise a CSV it has read into context will produce plausible aggregates, whereas a model that runs a GROUP BY gets the actual ones. The workflow enforces it — schemas and column types come back before the query is written. Worth knowing the scan is bounded: three directory levels, files up to 2GB.

What it is

A local data runtime for AI agents, backed by DuckDB and shipped both as an MCP server and as a standalone CLI for interactive exploration. You register a folder of data files and the model queries them with real SQL; the files never leave your machine.

What you get
  • `scan_folder` — register every data file in a folder in one call
  • `list_datasets` — the registered datasets with their schemas and column types
  • `get_schema` — one table inspected in detail, with sample rows
  • `execute_sql` — real DuckDB SQL run against your files, with results that are ground truth from DuckDB rather than generated
  • Format coverage: CSV, Parquet, JSON, NDJSON, TSV and Excel (xlsx, xls, xlsm), files up to 2GB, folders scanned three levels deep, with hidden files and noise directories like `node_modules`, `target` and `.git` skipped automatically
  • A CLI on the same engine for interactive work, with dot commands to scan, list, inspect and focus datasets, tab completion after `FROM` and `JOIN`, and every result saved as a live `_last` view you can query further
Requirements

No account and no key for the SQL and MCP paths — those work with no model provider configured at all. Install with the shell installer on macOS and Linux, the PowerShell one on Windows, or `cargo install pipetable`. Register it with `claude mcp add pipetable pipetable mcp`, or configure `pipetable` with the `mcp` argument in any client. Natural-language querying in the CLI is the only optional extra, and it auto-detects whichever of `ANTHROPIC_API_KEY`, `OPENAI_API_KEY` (with `OPENAI_BASE_URL` for local endpoints) or a running Ollama it finds, in that order. MIT licensed.

Setup effort

One command — curl -fsSL https://pipetable.com/install | sh