CLIO Parquet
Let your agent read, inspect, and manipulate Apache Parquet datasets while you build analytics APIs, pipelines, and warehouse-backed features.
Overview
CLIO Parquet is a MCP server for the Build phase that exposes Apache Parquet file operations to LLM agents over stdio.
What is this MCP server?
- CLIO Parquet MCP (v2.2.3) for Apache Parquet file operations via agents
- Distributed as clio-kit on PyPI with stdio MCP transport
- Supports LLM-driven workflows on columnar analytics storage
- Sibling to other iowarp CLIO data servers (pandas, parallel-sort)
- Useful for local lake files and pipeline debugging before full warehouse deploy
- Published version 2.2.3 per server schema
- PyPI identifier clio-kit with stdio transport
- Source repository iowarp/clio-kit on GitHub
Community signal: 25 GitHub stars.
What problem does it solve?
Agents hallucinate Parquet APIs and break pipelines when you need reliable columnar file access during backend work.
Who is it for?
Builders using Parquet in lakes, batches, or local analytics who want MCP-native agent access in the IDE.
Skip if: Products with zero Parquet or columnar files and only live SQL against a hosted DB.
What do I get? / Deliverables
Once configured, your agent can call Parquet MCP tools to inspect and work with .parquet data as you build analytics features.
- Configured Parquet MCP server in your agent toolchain
- Structured agent access to Parquet inspection and manipulation
- Faster backend and pipeline iteration on columnar datasets
Recommended MCP Servers
Journey fit
How it compares
MCP Parquet file integration, not a cloud warehouse console or standalone ETL SaaS.
Common Questions / FAQ
Who is CLIO Parquet for?
Indie devs and small teams building data-backed products who store or exchange Parquet and use MCP-enabled coding agents.
When should I use CLIO Parquet?
Use it while implementing backends, pipelines, or analytics features that read or write Apache Parquet on accessible paths.
How do I add CLIO Parquet to my agent?
Install clio-kit v2.2.3 from PyPI, register io.github.iowarp/parquet-mcp with stdio in your MCP settings, and grant the process access to your Parquet files.