"A server for data engineering tasks, GPU clustering, nomad setup, and data pipeline management" matching MCP servers:

A Model Context Protocol (MCP) server that coordinates AI agents to transform database tables into Wikipedia-style data dictionaries.

Enables querying Polish data protection regulations, decisions, and guidelines from the UODO directly from AI assistants.

Provides a single-writer MCP server for a governance-grade knowledge base of markdown documents with version control and query capabilities.

Enables AI coding agents to triage BigQuery ML and BigQuery AI tasks, look up exact model and function syntax, and search 137 syntax cards, 14 reference guides, and 104 live-verified narratives via a zero-dependency stdio MCP interface.

Provides coding agents with a shared, Markdown-based engineering knowledge base to search, capture, create, and update internal conventions, API details, infrastructure configs, and development setup via lightweight MCP tools.

Enables searching and browsing a curated matrix of 100+ engineering tools by name, tag, or use case, and retrieving ready-to-use boilerplate files for 30+ popular stacks.

This MCP server enables searching Korean construction standards (KDS/KCS), laws from the Ministry of Government Legislation, administrative rules and interpretations, and optionally local water/wastewater design manuals to generate grounded evidence packages for engineering answers.

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 converting a public release-note URL into ordered upgrade tasks with source excerpts, anchors, confidence, and uncertainty, without executing release text or project code.

Analyzes code repositories and generates comprehensive documentation published to Google Docs with Mermaid diagrams.

A public MCP server demonstrating how an AI agent can retrieve database schemas and manage local OpenAPI contracts via tools for CRUD operations.

A repo-native stdio MCP server that helps AI agents and human users make better use of existing CE-RISE Digital Passport components, services, schemas, documentation, and deployment workflows.

An MCP server that converts Cursor agent plans into structured markdown task lists and organizes them in your repository, helping you track AI-generated plans and recommendations as actionable specifications.

Runs a local, file-based toolkit of 100 systems engineering skills that lets an AI assistant clean and map requirements, review specifications against ECSS, derive sub-requirements, check interfaces and budgets, and build verification and compliance matrices. It works on documents in a user-chosen local folder without sending customer data to a remote service or requiring an account or API key.

Provides AI assistants with structured access to an organization's engineering standards, practices, and processes through searchable knowledge base with CRUD operations and multi-dimensional organization.

Provides structured templates for security questionnaires, RFP responses, and competitive battlecards to help AI agents and sales engineers draft responses efficiently.

Enables AI coding agents to ground dependency setup, compatibility checks, and major-version migration advice in a project's real runtime, lockfiles, and config files. It parses local project state and npm registry metadata deterministically, then returns tag-pinned install commands, breaking changes, and upgrade steps rather than generic documentation.

Automatically crawls documentation websites, converts them to organized markdown files, and generates condensed cheat sheets. Intelligently categorizes content into tools/APIs and provides local-first access to downloaded documentation.

Provides AI assistants with specialized tools to interact with NIST's Open Security Controls Assessment Language (OSCAL) framework. It enables agents to retrieve schemas, explore models, and generate valid OSCAL documentation for security compliance automation.

Provides AI assistants with locally bundled tools to explore NIST OSCAL models, retrieve schemas, list community resources, and generate valid OSCAL templates for governance, risk, and compliance workflows.

Enables AI agents to subscribe to a deterministic daily ethics feed drawn from a four-module curriculum and retrieve read-only tools, resources, and reflection prompts covering principles, case studies, and codes of conduct.

Enables agents to search and enrich B2B companies and people, find verified work emails and US phone numbers, search job postings, and consult live documentation, exposing one tool per API endpoint.

Enables AI assistants to index, search, and retrieve engineering documentation, architecture, standards, and knowledge from the OpenHaus Engineering System via the Model Context Protocol.

A collection of MCP servers for Slack, Jira, Confluence, and release coordination to automate QA workflows and daily release status reporting.

A stage-gated MCP server for processing academic papers, enabling sequential extraction of paper sections, methodology, results, and report generation through a controlled workflow with enforced tool visibility across stages.

Automatically generates Architecture Decision Records (ADRs) from Claude Code conversations using Claude Opus. Features AI quality review, duplicate detection, and dependency graph.

Enables querying documents through a Langflow backend using natural language questions, providing an interface to interact with Langflow document Q\&A flows.

Enables LLM clients to access crop yield predictions from RGB imagery, topic-based PDF literature, and soil lab/moisture CSV data.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.