"Resources and Information on Mechanical 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.

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.

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.

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.

Answers whether a route ran late and by how much, with drill-down into individual trip delays and an audit trail of data-quality exclusions and duplicate resolutions.

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

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.

A tutorial implementation MCP server that enables analysis of CSV and Parquet files. Allows users to summarize data and query file information through natural language interactions.

A server providing PDF form manipulation tools via MCP's API, allowing users to find PDFs across directories, extract form field information, and visualize form fields in documents.

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.

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

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