"MCP-N8N integration or server" matching MCP servers:

MCP server for managing n8n workflows through AI assistants. Supports workflow CRUD operations, synchronization, inspection, and execution support for automation-focused workflows.

A Model Context Protocol (MCP) server that generates production-ready n8n workflows from natural language prompts. Transform simple descriptions into fully validated, deployable automation workflows using AI-powered code generation.

Reads passports, ID cards and driver's licences from a photo or scan and returns the printed fields and MRZ as structured JSON. One credit ($0.01) per recognised document; nothing is charged when no document is found.

Provides local, deterministic scoring and detailed auditing of text for AI-writing patterns via two read-only tools, with no network calls or language models.

Enables reranking documents by relevance to a query through a Cohere-compatible API, so existing rerank code drops in with only a base_url change. Calls are pay-per-call in USDC via x402 or covered by a free daily tier, with no account, GPU, or model download needed.

Enables Codex to consult Claude Code for a second opinion through a local stdio MCP server, exposing the ask_claude tool for text-only reviews and recommendations using existing Claude Code authentication. Codex can request findings and tradeoff analysis without allowing Claude to edit files or run commands.

Enables MCP-compatible AI assistants to run Python code on Google Colab GPU/TPU runtimes, supporting accelerators like T4, A100, H100, with background execution and Google Drive integration.

Enables AI agents to receive a structured verdict on whether a UI component need can be satisfied by existing shadcn/ui or 21st.dev components or requires a custom build, with coverage scoring against real component evidence and a Mobbin reference when needed.

Enables coding agents to submit their original request, proposed plan items, and observed repository conventions for per-item necessity scoring before making edits. It returns advisory keep, simplify, or review recommendations with reasons and evidence, without blocking or modifying the plan.

Industry 4.0 / IIoT AI agents for manufacturing: explain OEE losses per shift, triage machine alarms, find downtime root causes, prepare predictive-maintenance jobs and write shift reports from Unified Namespace (UNS), MQTT or historian data. Plus an industrial AI-readiness diagnostic and a vendor-neutral digital-transformation advisor. Works in Claude Code, Codex and any MCP client.

Free Industry 4.0 / IIoT tools, no API key or account: lint MQTT Sparkplug B topics, check a Unified Namespace (UNS) for naming and hierarchy problems, and run an industrial AI-readiness diagnostic for manufacturing digital transformation and smart-factory projects. Works in Claude Code, Codex and any MCP client.

Enables document parsing into structured, confidence-scored fields via the scan tool, working with any MCP host like Claude Desktop or Cursor.

MCP server exposing NaN API media tools (image generation/editing, text-to-speech, speech-to-text, embeddings, reranking) for any MCP-compatible client.

Allows Claude Code to request an independent code review from Google Antigravity (Gemini, Claude, or GPT-OSS) via the Antigravity CLI, providing a second opinion on plans or diffs.

Enables OCR-enabled PDF to Markdown conversion in Claude Code, with per-page inspection and hybrid handling of text, scanned, mixed, and blank pages via a local vision-model endpoint.

Enables MCP agents to evaluate content against typed questions (yes/no, choice, or score) in a single parallel call.

Turn a sentence into an image or a short video on your machine.

Provides structured OCR with verified bounding boxes using the space-ocr API. Allows extraction of structured fields from document images and listing of built-in templates.

Provides access to Agno framework documentation for AI agents, enabling search and retrieval of SDK references, API endpoints, code examples, and integration guides through MCP-compatible tools.

Routes questions to multiple AI models (local and cloud) and synthesizes their answers in individual, categorized, or deconflicted modes.