Parquet MCP Server
 
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MCP

Parquet MCP Server: context for faster AI-driven integration

Context is the multiplier—give AI the right inputs, and it writes better code and ships faster.

Tired of AI hallucinations and debugging? CData MCP Server for Parquet connects AI to your actual Parquet schema—no guessing, no fixing, just working code.

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Decorative Icon Parquet Logo

Unmatched productivity with reliable results—AI validates against your real Parquet data model

10x faster development—because AI understands your actual Parquet structure before writing a single line of code.

Ask Claude a question
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Create a Java ETL application that retrieves Parquet data and stores it in a local database
I'll help you create that Parquet ETL application in Java. First, let me query the Parquet structure and data to understand what we're working with.

View result from parquet_mcp_run_query from ParquetMCP (local)

...

Context is everything: AI + MCP

Why this works better than AI guessing API docs and failing every time


Generic AI guesses

This architecture is constrained by how data systems actually behave.

  • MCP provides real schema and data from Parquet
  • No more guessing how to query/call with wrong parameters or invented field names
  • Outputs are deterministic and testable

Works in real environments

You stay in control. The system removes the guesswork.

  • Compatible with CData Parquet JDBC, ADO.NET, ODBC, and Python Drivers
  • Supports enterprise auth, TLS, proxies, and OS differences
  • Designed for local dev, CI and production workflows

What teams see after adopting it

Measurable impact from day one

  • Faster time to first working integration
  • Fewer production surprises
  • Less time lost to configuration debugging
  • Fewer repetitive support questions

AI connects, explores, ships. You just direct.

Build with MCP Server for Parquet. Deploy with CData Drivers.

01
Connect

Connect to your data source using MCP Server

Provide:

  • Data source connection via the MCP Server UI

Get:

  • Schema discovery from a live connection to your source
  • Standardized SQL and stored procedure access
02
Explore

Explore and validate on AI coding tools like Cursor, Claude Code, GitHub Copilot

Provide:

  • Natural language queries
  • Prompts for application code requirements

Get:

  • Accurate queries based on live-schema from the source
  • Precise filtering and JOINs by retrieving sample values, picklist values
  • Validation during exploration, no guessing
  • Executable code for your data-driven applications
03
Deploy

Build integrations that survive production

  • Schema and syntax parity between MCP and CData drivers in production
  • Standardize integration patterns across services and teams
# AI helped write this. No AI runs it.

import cdata.parquet as cdata_parquet

conn = cdata_parquet.connect("User=...;Password=...")
cursor = conn.cursor()

cursor.execute("""
SELECT Id, Name, Industry, AnnualRevenue
FROM Account
WHERE AnnualRevenue > 1000000
ORDER BY AnnualRevenue DESC
""")

for row in cursor.fetchall():
process_account(row)

# Runs as scheduled job, cron, or service
# No LLM. No tokens. Just reliable execution.

Integrate with Parquet 10x faster with CData MCP server

Build data-driven applications connected to Parquet

Your Java, .NET, C/C++, Go, Node.js, PHP, or Python application can now interact with Parquet data faster than ever. The biggest challenges in data-driven application development were schema discovery and query tuning. AI can handle that.

Prototype complex queries before production

Test JOINs, filters, and aggregations in your AI coding environment. Validated queries for Parquet integrate directly into your applications without modification.

Automate Parquet ETL scripts for your data warehouse

Generate scripts that query, transform, and sync Parquet data to your warehouse. Built-in support for incremental updates across data sources.

Ready to get started? Try AI Coding + Parquet today!

No more guessing, start shipping!

Download Trial
buy now