"Understanding Vite running status in terminal commands" matching MCP servers:

mcp-clickhouse-long-running

An MCP server for Claude Desktop that allows users to check the status of their Airbyte connections.

Exposes account risk status from a fraud detection pipeline, returning risk scores, flagged transaction history, and LLM-generated explanations via the get_account_risk_status tool.

MCP server for configuring the konsol Frappe app via the CLI. Enables AI clients to manage dimensions, measures, fact tables, connectors, and schemas using natural language.

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

Enables preparing data, building and running Mplus input files, and reading the output back as tables, including fit indices, parameter estimates, and warnings.

Bridges Claude (or any MCP client) to manufacturing data via the i3X standard, enabling natural language queries about equipment status, hierarchy, and historical trends.

Enables monitoring and managing Debezium/Kafka Connect CDC connectors, including status, configuration, lifecycle operations, and security controls, through natural language in MCP-compatible clients.

A server that connects to PostgreSQL databases and provides tools for safely exploring schemas, running read-only SQL queries, and performing data analysis with pre-built templates.

Enables structured document understanding of local PDFs via a dual-extract MCP server, combining MinerU text/layout and Qwen3-VL vision with fusion adjudication for field extraction, tables, formulas, and validation. Fully local and offline.

A Model Context Protocol server that integrates Garmin Connect data with LLMs to provide personalized running analysis and training plans. It enables users to monitor performance metrics, manage training loads, and receive data-driven workout suggestions based on health indicators like VO2 Max and recovery status.

Investidor10 MCP Server: A Model Context Protocol (MCP) server for interacting with the Status Invest API. Provides tools for fetching stock data and indicators, with a layered architecture and data validation using Zod

A Model Context Protocol server implementation that enables AI assistants to interact with Apollo.io data, performing operations like people/organization enrichment, searching, and retrieving job postings through natural language commands.

Provides access to comprehensive financial market data from Massive.com API, including real-time and historical data for stocks, options, forex, crypto, trades, quotes, market snapshots, ticker details, dividends, splits, fundamentals, and market status through an LLM-friendly interface.

Exposes Microsoft Fabric operations as MCP tools, with 105 tools across 17 domains including lakehouses, warehouses, notebooks, pipelines, and real-time analytics, handling long-running operations and supporting multiple authentication modes.

Enables AI agents to create, manage, and monitor two-way data syncs between platforms like Airtable, Webflow, HubSpot, Salesforce, Notion, and Postgres, including mapping fields, running syncs, and troubleshooting record-level issues.

Remote MCP server for querying financial index data, running backtests, and building/deploying systematic investment strategies from any MCP-capable client.

Enables managing orchestration workflows through tools for creating, preflighting, linting, and running sequences, checking instance status and outputs, sending signals, retrying instances, and inspecting DLQ and usage.

Connects MCP clients to databases like Snowflake, BigQuery, and Databricks through Alkemi's data platform, enabling natural language database queries with proper schema understanding and metadata management for consistent team-wide access.

Connects MCP clients such as Claude Desktop and Claude Code to Opteryx so users can ask questions about their data in plain English, discovering datasets, reading schemas, running queries, profiling columns, and validating SQL. Every call runs as the token's own user and against only the data that user can see.