"Understanding Reverse Processes or Reverse 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.

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.

MCP server that enables AI agents to parse, validate, and reverse ISO 20022 bank statements, with tools for discovering message types and return reasons.

Enables AI coding agents to import xlsx config tables, audit growth curves, run battle and gacha simulations, and reverse-engineer game spreadsheet formulas through 17 read-only tools backed by deterministic, zero-dependency pure-function engines.

Enables AI assistants to manage CData Sync operations, including data synchronization jobs, connections, and ETL processes through stdio or HTTP transports. It provides tools for executing jobs, monitoring real-time progress via Server-Sent Events, and handling comprehensive workspace configurations.

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.

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.

A Model Context Protocol server that extracts and processes content from PDF documents, providing text extraction, metadata retrieval, page-level processing, and PDF validation capabilities.

An MCP server for data reconciliation between MySQL and Snowflake, enabling LLM agents to validate data integrity during migrations and ETL processes.

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.

This MCP server enables interaction with the Polytomic API to manage data connections, models, and synchronization tasks. It supports both Reverse ETL and ELT operations, allowing users to trigger syncs, monitor execution status, and configure data pipelines.

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.

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.