"Hugging: Exploration and Resources" matching MCP servers:

Enables autonomous data exploration on .csv-based datasets, providing intelligent insights with minimal effort.

Exposes a synthetic issue tracker and pipeline warehouse as callable tools so an agent can answer operational questions about tickets, pipeline health, runs, incidents, and governed metrics with every claim cited to the exact tool call it came from. All writes are proposal-only, requiring human approval through a gated apply path that logs each step for audit.

Enables users to convert a vendor partner directory (such as PartnerPage or the Shopify Partner Directory) or a trade body member list into one company row per listing, each carrying website, domain, tier, tags, and location for Clay-ready output. It runs the extraction on Apify, returning dataset rows for up to 20 directory URLs with optional deduping, detail enrichment, and a cost cap.

Enables agent tools to validate queries, assess token costs, and extract structured datasets from unstructured text using multiple LLM providers.

This server facilitates scalable discovery and execution of OpenAPI endpoints using semantic search and high-performance processing, overcoming limitations of large spec handling for streamlined API interactions.

Exposes local DuckDB pizza sales data as MCP tools for querying store-level and total sales metrics via natural language, enabling local LLMs to perform real-time analytical queries.

Enables AI to interact with Microsoft Access (.mdb, .accdb) and SQLite 3 databases, including read-only support for Access 97 databases. Supports executing SQL queries, importing/export data from CSV and Excel files, and storing notes about database files.

Provides stock market data and computes technical indicators like EMA, MACD, and RSI across multiple timeframes using the LongPort API and TA-Lib. It enables users to perform detailed financial analysis and multi-timeframe market reviews through standard MCP tools.

Enables schema-aware exploration of JSON data by uploading samples, flattening nested structures, and using heuristic search with token overlap and fuzzy matching to find field paths for target names, accelerating ETL and API onboarding workflows.

Enables users to preprocess, analyze, and visualize CSV data through comprehensive tools for data manipulation, statistical analysis, and graph generation.

Provides MCP tools for scanning, redacting, and packaging dataset/model directories for public release, leveraging Presidio for PII detection and generating Hugging Face cards and EU AI Act training-data summaries.

Provides a stateful local Spark session for running PySpark and SQL cells, enabling local data exploration before deploying to Microsoft Fabric.

Provides GraphQL access to Airtable and Google Sheets data, enabling natural language queries for data exploration, schema introspection, and record management.

It gives Claude Code, Cursor, and Codex structured workflows and an MCP server for building data pipelines with dlt - covering REST API ingestion, transformations, data quality, and exploration.

An MCP server for executing Malloy queries and managing Malloy resources.

Enables AI assistants to discover and invoke 445 Salesforce Marketing Cloud Next, Data 360, and Data 360 Connect API endpoints, plus SOQL queries, object describe, REST exploration, org limits, and record/metadata CRUD, with shared OAuth authentication and safety gates.

Minimalistic MCP server that lets AI assistants inspect, quality-check, and clean CSV datasets through tools, resources, and prompts, without needing local file access.

Production-ready MCP server that enables AI assistants to seamlessly interact with SAP Datasphere environments for real tenant data discovery, metadata exploration, analytics operations, ETL data extraction, database user management, data lineage analysis, and column-level data profiling.

An MCP server for dataset exploration and analysis, enabling LLM clients to perform summary, correlation, distribution, missing value analysis, data cleaning, and statistical tests directly on CSV files.

Enables querying and managing SAP Cloud Platform Integration resources such as packages, integration flows, message processing logs, security material, and artifact lifecycle through the CPI OData v2 API.

Enables users to interact with Cube's AI agent for real-time analytics and data exploration through a chat interface. It supports generating data visualizations, performing SQL queries, and analyzing trends using natural language.

Enables querying and analyzing blockchain data from Dune Analytics with Polars-optimized workflows, including schema discovery, Sui package exploration, and query management through natural language.

Enables AI assistants to connect to StarRocks databases for direct SQL execution, schema exploration, and data visualization.

A FastMCP server for natural language data analysis that allows users to load CSV files, execute Python code with pandas and numpy, and generate matplotlib visualizations. It provides a suite of tools for data exploration, statistical summaries, and querying datasets through MCP-compatible clients.

Enables AI assistants to query and interact with Treasure Data through a secure interface, supporting SQL queries, database exploration, CDP segment management, and workflow monitoring across multiple regions.

Read-only MCP server for ZenMoney that syncs financial data into memory and exposes it through tools and resources, enabling agentic analysis of transactions and spending patterns.

Enables access to ForexFactory economic calendar data through MCP resources and tools. Supports retrieving economic events by time periods for integration with trading assistants and agentic workflows.

Bootstrap MCP server for future data exploration and querying across multiple databases, currently only provides a hello_world tool with no actual data connectivity.

A Model Context Protocol server that enables Claude to load CSV files and execute JavaScript data analysis scripts for advanced data exploration and insights generation.

Exposes safe JSON contracts for validating and preparing datasets, running supervised fine-tuning, preference learning, RL, distillation, and evaluation, and managing checkpoints and exports through the Tinker SDK and Cookbook. It lets agents discover models and Hugging Face datasets, plan and execute training recipes, compare experiments, and export selected models.