"How to query data in Snowflake" matching MCP servers:

Enables AI assistants to securely connect to Snowflake data warehouses and execute SQL queries through natural language interactions. Supports multiple authentication methods and provides formatted query results with built-in security controls.

Open-source agentic schema layer. Define metrics once in YAML, query governed data from any warehouse (Snowflake, BigQuery, Databricks, PostgreSQL, DuckDB) via MCP.

A semantic layer query engine with MCP support, enabling AI assistants to query structured data through natural language and declarative interfaces.

Enables interaction with Snowflake through Cortex AI services including search, analyst, and agents for querying structured and unstructured data, plus SQL execution and object management capabilities.

Enables Claude to interact with Snowflake data warehouses through natural language for executing SQL queries, exploring schemas, and monitoring data freshness. It streamlines data analysis workflows by bringing Snowflake capabilities directly into the AI conversation.

Integrates Snowflake Cortex AI features like Cortex Search and Analyst into the MCP ecosystem, enabling retrieval-augmented generation and structured data analysis. It also provides tools for chat completions and agentic orchestration using Snowflake-hosted large language models.

Enables querying and analyzing user, product, and order data with filtering capabilities and real-time statistics. Supports WebSocket connections to XiaoZhi AI platform with automatic reconnection.

Enables users to query both structured and unstructured data in Snowflake using Cortex AI features including Cortex Search for RAG applications, Cortex Analyst for semantic modeling, and Cortex Agent for agentic orchestration across data types.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

An MCP server that runs Apify scraping actors with quality-gated validation, scoring, and retries, returning embedding-ready chunks for AI agents.

DC Hub is the live Model Context Protocol layer for data-center and energy infrastructure. AI agents call 91 tools across 22,900+ facilities for land+power, grid, interconnection, fiber, and Capacity Source — with as_of timestamps. Connect: https://dchub.cloud/connect · Docs: https://dchub.cloud/ai-agents · Endpoint: https://dchub.cloud/mcp · Pro $99/mo (never $299/Founding).

🤖 Enable AI assistants (Claude, Cursor) to monitor, diagnose, and query Amazon Redshift using this MCP server and the AWS Data API.

Search 1,500+ pre-verified, cited knowledge units across 16 domains. 6 tools: query, batch query, get unit, list domains, suggest topics, report issues. Free, no API key required.

Natural language to SQL engine with multi-connector support (PostgreSQL, MySQL, Snowflake, BigQuery, DuckDB), document QA, semantic caching, and self-hosted MCP server.

Query SQL databases (SQLite, PostgreSQL, BigQuery, Databricks) in natural language through a business semantic layer — glossary, metrics, and a data dictionary grounded against your real schema. Read-only by default, with an embedded SQLite + sqlite-vec metadata store and no external infra required.

A basic MCP server setup guide demonstrating how to configure and run Python-based MCP servers with integration examples for Bright Data web scraping and Apify Actors for product data collection.

Enables AI agents to query verified-emissions knowledge graphs in natural language and validate data against supported standards, with metered billing and optional verified identity.

Enables AI agents to find and query real-time GitHub coding bounties with built-in scam filtering, supporting listing, matching, and detailed bounty retrieval.

Enables LLMs to query structured statistical data from the Swiss Federal Archives' Linked Data platform (LINDAS) by translating natural language questions into SPARQL queries against RDF data cubes.