"Apache Parquet" matching MCP servers:

A proof of concept MCP server for Apache OFBiz

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).

MCP server that integrates Apache Ranger authorization with RAG, enforcing Ranger policies to control access to knowledge bases before forwarding queries to RAG Studio.

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.

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.

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

MCP server for tabular data retrieval that indexes local CSV, Excel, Parquet, and JSONL files once and answers questions via column profiles, filtered rows, server-side aggregations, and joins, drastically reducing token usage for large datasets.

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.

A Model Context Protocol server that enables large language models to interact with Apache Superset databases through REST API, supporting database queries, table lookups, field information retrieval, and SQL execution.

Monitor and manage Apache Airflow clusters through natural language queries via MCP tools: DAG inspection, task monitoring, health checks, and cluster analytics without API complexity. * Guide: https://call518.medium.com/mcp-airflow-api-a-model-context-protocol-mcp-server-for-apache-airflow-5dfdfb2

Manage, explore, transform and join data in Kafka topics across multiple clusters using different flavours of Apache Kafka via Lenses.io (including the free Community Edition)

This read-only MCP Server allows you to connect to Apache Spark data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out the first managed MCP platform: CData Connect AI (https://www.cdata.com/ai/).

Reference MCP server for OCL, an open standard giving AI agents structured, scoped business meaning for Odoo data. Serves published example context packs via get_context, resolve_noun, and explain_field (stdio, Python, Apache-2.0).

The Wanaku MCP Router stands between autonomous AI agents and your enterprise systems. Wanaku leverages proven integration technology, like Apache Camel, to set up and manage hundreds or thousands of integrations.

An MCP server that wraps Apache Tika to extract text, metadata, and more from documents. It supports local files and optional remote URLs with SSRF protections.

Enables natural language querying of Apache Doris databases via LLM-powered SQL generation, execution, and metadata management through the MCP protocol.

A Model Context Protocol server that connects AI agents to Apache Jena, enabling them to execute SPARQL queries and updates against RDF data stored in Jena Fuseki.

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.

Enables AI models to publish and consume messages from Apache Kafka topics through a standardized interface, making it easy to integrate Kafka messaging with LLM and agent applications.

A Python server that enables AI assistants to perform hybrid search queries against Apache Solr indexes through the Model Context Protocol, combining keyword precision with vector-based semantic understanding.