Pipetable MCP Server

Pipetable

Gives your AI coding tool real data access.

Pipetable demo Point it at a folder of CSV, Parquet, JSON, or TSV files — your AI can now query them with real SQL instead of hallucinating.

Works as an MCP server for Claude Code, Cursor, RooCode, and Copilot. Also ships as a standalone CLI for interactive data exploration. Powered by DuckDB. Files never leave your machine.

MIT licensed.

Install

# macOS / Linux
curl -fsSL https://pipetable.com/install | sh

# Windows
irm https://pipetable.com/install.ps1 | iex

# Rust
cargo install pipetable

MCP server setup

Claude Code

claude mcp add pipetable pipetable mcp

Cursor / RooCode

{
  "mcpServers": {
    "pipetable": {
      "command": "pipetable",
      "args": ["mcp"]
    }
  }
}

VS Code (Copilot)

{
  "servers": {
    "pipetable": {
      "type": "stdio",
      "command": "pipetable",
      "args": ["mcp"]
    }
  }
}

Once configured, your AI can:

scan_folder — register all data files in a folder
list_datasets — see schemas and column types
get_schema — inspect a specific table with sample rows
execute_sql — run real DuckDB SQL against your files

Results are ground truth from DuckDB, not generated.

CLI

pipetable ~/data/

SQL and natural language at the > prompt. SQL always works. Natural language requires Ollama running locally.

> SELECT region, SUM(revenue) AS total FROM sales GROUP BY 1 ORDER BY 2 DESC

4 row(s)

region  total
─────────────
EU      141000
US       32000
APAC     17000

> show me top 5 customers by revenue
Using: customers, sales
Thinking.....
SELECT c.name, SUM(s.revenue) AS total FROM customers c
JOIN sales s ON s.customer_id = c.id
GROUP BY c.name ORDER BY total DESC LIMIT 5
...
→ piped as _last

Piping results

Every query saves its result as _last — a live DuckDB view you can query further:

> SELECT * FROM sales WHERE region = 'EU'
...
→ piped as _last

> show me top 3 from _last
Using: _last
Thinking.....

Dot commands

Command	Description
`.scan <path>`	Load a folder or file (Tab completes)
`.datasets`	List loaded datasets
`.schema <name>`	Columns + sample rows
`.drop <name>`	Remove a dataset from the session
`.use <n1> <n2>`	Focus NL queries on specific datasets
`.remove <name>`	Remove from focus
`.clear`	Reset focus to all datasets
`.model <name>`	Switch Ollama model
`.help`	Show help

Tab completes dataset names after FROM, JOIN, .schema, .drop, .use.

One-shot query

pipetable ask "who has the highest revenue?" ~/data/
pipetable ask "SELECT * FROM sales LIMIT 5" ~/data/

Natural language (optional)

Set any one of these — pipetable auto-detects:

# Claude (best quality)
export ANTHROPIC_API_KEY=sk-ant-...

# OpenAI or any compatible API (LM Studio, Groq, Together, etc.)
export OPENAI_API_KEY=sk-...
export OPENAI_BASE_URL=http://localhost:1234  # optional, for local endpoints

# Ollama (local, no key needed)
ollama pull qwen2.5-coder:1.5b
ollama serve

Priority: Anthropic → OpenAI-compatible → Ollama. SQL and MCP work without any of them.

Supported formats

CSV, Parquet, JSON, NDJSON, TSV, Excel (xlsx, xls, xlsm). Files up to 2GB. Folders scanned up to 3 levels deep. Hidden files and common noise directories (node_modules, target, .git) are skipped automatically.

License

MIT

Documentation