"Information about Parquet" matching MCP servers:

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 search and ask questions about documents stored in AnythingLLM workspaces through MCP tools.

Enables AI assistants to search and retrieve transcripts from the Entra.Chat podcast about Microsoft Entra ID, with timestamped YouTube links and guest information.

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 allows AI agents to perform WHOIS lookups, enabling users to directly ask the AI about domain availability, ownership, registration details, and other domain information.

Provides LLMs with specialized knowledge about fountain pen inks, enabling intelligent ink search, color matching, and recommendations through various tools.

A Model Context Protocol server that provides knowledge graph-based persistent memory for LLMs, allowing them to store, retrieve, and reason about information across multiple conversations and sessions.

Provides personalized Data Engineering learning updates by fetching recent news about DE concepts, patterns, and technologies, while tracking user knowledge to present only new information relevant to their learning journey.

A server that connects MCP clients (Claude, Cursor, Windsurf, etc.) to EventCatalog instances, allowing users to ask questions about their event-driven architecture and get insights about domains, services, and message schemas directly within their development tools.

A Model Context Protocol server that provides natural language access to Cortex API, enabling users to query information about their system's structure and make faster decisions about services, incidents, and team responsibilities.

Exposes a personalized AI agent that reads your resume and provides intelligent responses about your professional background through a standardized MCP server interface with RAG capabilities.

MCP Memory is an MCP Server that gives MCP Clients the ability to remember information about users across conversations. It uses vector search technology to find relevant memories based on meaning, not just keywords.

MCP server for local-first RAG over Obsidian vaults, enabling AI agents to search and ask questions about notes with grounded citations.

Enables users to query information about LPDP scholarship financial disbursement using RAG with Pinecone vector search and Gemini 2.0 Flash, answering questions about funding components, deadlines, living allowances, and required documents.

A Model Context Protocol (MCP) server that enables LLMs to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB. Ask LLMs questions about the dataset as a whole or about specific documents.

An MCP server that provides deep knowledge about OpenAI APIs and SDKs, enabling users to query technical information through various MCP clients including ChatGPT Deep Research, Cursor, and OpenAI Responses API.

A persistent memory system using a local knowledge graph that enables Claude to remember information about users across chats, with advanced search, graph traversal, and filtering capabilities for entities, relations, and observations.

A Model Context Protocol server implementation that connects AI assistants to DuckDB, enabling them to query and analyze data from various sources including CSV, Parquet, JSON, and cloud storage through SQL.

Enables local-first, source-cited AI answers about your TTRPG campaign lore from your own documents, usable in Discord, Foundry VTT, or any MCP client while refusing to invent information outside your sources.