"Creating an Automation System Using n8n" matching MCP servers:

A Model Context Protocol server that provides AI assistants with comprehensive access to n8n node documentation, properties, and operations for effective workflow automation.

Wraps n8n with an MCP server and vector storage to enable semantic search, management, and execution of automated workflows. It integrates with other tools to make workflows searchable and orchestratable within a larger automation ecosystem.

About Your task manager is the best agent memory you're not using. An agent-native context layer over your existing task app.

Local-first knowledge system for reasoning agents, exposing facts, evidence, documents, retrieval, and audit history through a thin stdio MCP server.

A Model Context Protocol server that allows AI agents to interact with n8n workflows through natural language, enabling workflow management and execution via SSE connections.

MCP server for a modular RAG system that enables natural language question answering over enterprise documents with intent-aware routing, adaptive retrieval, and citation-backed responses.

A Model Context Protocol server that allows AI assistants to save structured information to n8n via webhooks for storage, indexing, and retrieval.

Pre-trade risk validation and position sizing for AI trading agents via G-formula and Iron Fist.

Enables integration with financial transaction data through REST APIs, PostgreSQL databases, and document storage systems. Demonstrates agentic AI capabilities by connecting to Alpha Vantage API and managing financial data through natural language interactions.

Provides AI clients with a persistent, readable and writable long-term memory layer over MCP, enabling creation, retrieval, tagging, updating, and deletion of memories across sessions.

A Retrieval-Augmented Generation system that enables uploading, processing, and semantic search of PDF documents using vector embeddings and FAISS indexing for context-aware question answering.

An MCP-compatible RAG backend using LangGraph and FastAPI, enabling chaining of AI model logic with document context search.

Enables AI applications to access and contextualize organizational knowledge sources including GitHub repositories and internal documentation through standardized MCP protocol integration. Features OAuth 2.1 authentication, vector-based semantic search, and optimized context chunking for enterprise development workflows.

An advanced MCP server that implements sophisticated sequential thinking using a coordinated team of specialized AI agents (Planner, Researcher, Analyzer, Critic, Synthesizer) to deeply analyze problems and provide high-quality, structured reasoning.

An automated system that processes raw text through a three-stage pipeline to assess data quality, enhance information, and generate structured knowledge graphs. It provides tools for triple extraction with confidence scoring and creates interactive HTML visualizations of the resulting graph.

A vector search system that enables semantic retrieval of document chunks using MongoDB Atlas Vector Search and Voyage AI embeddings, allowing users to search documents by meaning rather than just keywords.

Enables querying a Neo4j knowledge graph about Wroclaw University of Science and Technology using natural language. Converts user questions into Cypher queries and retrieves contextual information through an intelligent RAG pipeline with LLM-powered query routing.

Transforms code repositories and development documentation into a queryable Neo4j knowledge graph, enabling AI assistants to perform intelligent code analysis, dependency mapping, impact assessment, and automated documentation generation across 15+ programming languages.

Enables intelligent data analysis and querying of Snowflake databases through specialized AI agents. Features 20+ tools for data operations, lineage tracing, usage analysis, and performance optimization with multi-agent architecture.

A Model Context Protocol (MCP) server that converts natural language queries into SQL statements, allowing users to query MySQL databases using conversational language instead of writing SQL code.