"Apache Parquet" matching MCP servers:

This read-only MCP Server allows you to connect to Parquet data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out our MCP Server for Parquet (https://www.cdata.com/drivers/parquet/download/mcp).

Enables querying and managing Apache Druid datasources through natural language, including SQL queries, datasource exploration, and cluster connectivity testing.

Connects LLM agents to Apache Atlas for searching, tracing lineage, and managing metadata. Read-only by default, with optional write mode for creating entities and classifications.

An MCP server that enables users to interact with Apache Doris databases through natural language, supporting query and management operations.

A powerful MCP (Model Control Protocol) server that provides tools for manipulating and analyzing Parquet files. This server is designed to work with Claude Desktop and offers four main functionalities:

Natural Language Analytics MCP server for Apache Druid. With this enterprise ready server in Java, one can query Apache Druid by natural language queries. Furthermore, the complete management like data loading of the Time Series Database Druid can be done with natural language commands.

This read-only MCP Server allows you to connect to Apache Hbase data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out our MCP Server for Apache Hbase (https://www.cdata.com/drivers/hbase/download/mcp).

This read-only MCP Server allows you to connect to Apache CouchDB data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out our MCP Server for Apache CouchDB (https://www.cdata.com/drivers/couchdb/download/mcp).

Enables AI assistants to interact with local CSV and Parquet data files through natural language queries, facilitating tasks like summarizing datasets or retrieving specific information.

Enables natural-language querying of large Excel, Access, and Parquet datasets, returning executed SQL with results for verification. Exposes tools for status checking, table listing, and asking questions through MCP.

Provides full-text search and retrieval tools for Apache Spark documentation using SQLite FTS5 with BM25 ranking. It enables AI assistants to efficiently search, filter by section, and read specific Spark documentation pages.

Enables MCP-compatible AI agents to run structured searches over India's court judgments, tribunal matters, and legislation directly from Hugging Face parquet files, with automatic license attribution.

Local-first MCP server providing semantic search over library docs, fully offline. Single Go binary speaks MCP over stdio against a vector index pinned to the binary version. Like Context7 with the internet turned off. Apache 2.0. Linux + macOS, also available as a container image.

MCP server for answering questions about Apache Software Foundation policies, offering tools to list, retrieve, and search policy documents on releases, licensing, branding, security, infrastructure, and incubator topics.

Enables AI assistants to answer questions about Apache Iggy using the project's current guides and Rust API documentation, with search, retrieval, and documentation analysis tools.

MCP server for chatting with physical-world data from robotics, drones, automotive, and IoT sources using natural language. It generates auditable SQL queries over Apache Arrow/DuckDB to let you analyze, summarize, and build data pipelines.

Provides managed Apache Solr search as MCP tools, enabling hybrid retrieval, server-side GPU embeddings, document indexing, and grounded RAG answers without managing infrastructure.

Lets AI assistants connect to Apache Solr deployments to enumerate collections and schemas, run full-text and filtered queries, apply faceting and sorting, and issue SQL or vector-semantic searches without building custom API glue. Communicates over stdio or SSE and requires minimal environment configuration instead of app-specific credentials or mandatory ZooKeeper wiring.

A tutorial implementation MCP server that enables analysis of CSV and Parquet files. Allows users to summarize data and query file information through natural language interactions.

Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.