"Understanding Prompts or Prompt Engineering" matching MCP servers:

Connects LLMs to Israeli civil engineering databases, including the Blue Book specs, Dekel 2026 pricing, Excel BOQ parsing, and tender arbitrage for unbalanced bidding.

Enables local and offline XLSX header cataloging, candidate relationship inference, and Oracle SELECT/WITH SQL generation without accessing databases or executing SQL.

Enables AI clients to investigate data engineering workflows by exposing tools for job status, logs, schema lookup, read-only SQL, and searching documentation/incidents, along with resources and prompts for ETL failure analysis.

Exposes the automated feature engineering LangGraph agent as MCP tools, enabling dataset profiling, feature planning, and feature code execution through MCP-compatible hosts.

Enables AI agents to inspect a mock data platform locally via SQLite, using read-only tools for schemas, row counts, freshness, queries, and pipeline status without cloud credentials.

Provides full execution and management capabilities for Microsoft Fabric Data Engineering workloads, including notebooks, pipelines, Lakehouses, and Spark jobs. It enables users to trigger runs, monitor status, manage workspace items, and configure job schedules through natural language.

Web extraction MCP server for AI agents. Extract structured data from any URL with built-in Cloudflare bypass, JavaScript rendering, and intelligent parsing. Returns clean markdown or JSON.

A unified Model Context Protocol server for data engineering and analytics that lets LLM clients query, profile, transform, and visualize data using DuckDB and PySpark, while keeping raw data out of the model's context through read-only, bounded, and audited operations.

Enables AI agents to safely query and explore Apache Impala clusters with read-only SQL, metadata, statistics, cluster overview, and analysis prompt templates.

MCP server that provides AI assistants direct access to the Apache Zeppelin REST API for data engineering workflows, enabling notebook and paragraph management, execution, and interpreter control.

Enables users to convert structured data into Token-Oriented Object Notation (TOON) to reduce LLM token usage and costs by up to 70%. It provides tools for encoding, decoding, and analyzing data formats like JSON, CSV, and XML to optimize prompt efficiency.

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

Generates and deploys Cloudera Data Engineering (CDE) Airflow jobs from ODCS data contracts, validating live table data in CDW against schema and data-quality rules without WAP staging.

Assists with SQL dialect transpilation, PySpark code optimization, and AWS Glue integration for data engineering tasks.

Enables AI agents to interact with Microsoft Fabric by exposing tools for managing workspaces, notebooks, SQL queries, pipelines, and Livy Spark sessions. It provides a comprehensive set of operations for data engineering and analytics tasks using standard Azure authentication.

A hybrid Fivetran MCP server with 23 native tools for common data engineering tasks and two universal tools (search/execute) that unlock all 161 Fivetran REST API endpoints.

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.

It interacts with the Kaggle API to provide tools for searching and downloading datasets, and a prompt for generating EDA notebooks.

A Model Context Protocol server enabling product searches across e-commerce platforms, price history tracking, and product specification-based searches using natural language prompts.

Enables AI applications to integrate with Tableau through tools, resources, and prompts for querying data, exploring content, and retrieving visualizations from Tableau workbooks and datasources.