"An automation tool for workflows and integrations (n8n software)" matching MCP servers:

Run YAML workflows as MCP tools so agents can automate real tasks with one server.

Enables MCP clients to run declarative agents and DAG workflows as plain tools, with parallel nodes, review loops, and per-run least-privilege sandboxing.

Run dynamic, multi-agent workflow scripts — agent(), parallel(), pipeline() — over real coding agents (Claude Code and OpenAI Codex), with deterministic journaling, resume, token budgets, and git-worktree isolation.

Enables AI agents to discover, understand, and execute complex multi-step workflows defined in YAML files through the Model Context Protocol.

Enables MCP clients to run complete browser workflows through a single run_workflow tool, with anti-detection backends, intelligent element location, automatic retries, and session persistence.

Enables durable multi-agent workflow runs for Codex with a live terminal dashboard, restartable agents, and bug-finding templates, all controllable through natural language.

A Model Context Protocol server that enables AI assistants to interact with n8n workflows through natural language, supporting actions like listing, creating, updating, executing and monitoring workflows.

MCP router that fronts your other MCP servers behind a few small generic tools, so their tool definitions stay out of the context window and are discovered on demand.

Graph-based tool retrieval for LLM agents. Builds a tool graph from OpenAPI/MCP specs and retrieves multi-step workflows via hybrid search (BM25 + graph traversal + embedding), recovering accuracy from 12% to 82% with 79% fewer tokens. Also works as an MCP Proxy to aggregate multiple servers behind 3 meta-tools.

Enables AI agents to create and manage video compilations through n8n workflows with smart query optimization, quality analysis, batch processing, and trusted channel filtering for platforms like YouTube.

Orchestrates Fireflies, Asana, and Notion MCP servers to automate end-to-end meeting workflows. Enables users to search meetings, extract action items, create tasks, and generate meeting documentation through natural language commands.

MCP server that routes natural language requests to structured tool calls using a LoRA-tuned small language model, with built-in validation, retry, and fallback recovery.

Run the open-source n8n-mcp locally and connect your n8n instance to MCP-compatible AI agents like Codex and Claude Code.

Enables ChatGPT to inspect Jobber data and manage controlled automations with scheduling, approval workflows, and audit trails.

Run provable, .slang-driven multi-agent workflows inside Claude Code. A deterministic executor enforces typed output contracts, tool scoping, and convergence, providing guarantees and auditability.

Enables AI agents to discover and execute tools via a secure MCP server with JWT authentication, RBAC, rate limiting, and audit logging.

A Model Context Protocol server that enables AI agents to interact with n8n workflows and automation tools through a standardized interface, allowing execution of workflows and access to n8n functions.

An MCP proxy that embeds tool definitions and returns only the top-k semantically relevant tools per query, reducing context usage and routing calls to upstream MCP servers.

Central registry for AI tools with vector search and knowledge graph, enabling intelligent tool discovery, chaining, and execution via MCP.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

A single MCP server that provides dynamic access to thousands of APIs through discovery and proxy, eliminating the need to install and configure multiple separate MCP servers.

Token-saving MCP proxy. Hides your MCP servers' tool lists behind a search tool and shrinks large tool results (GitHub, Playwright, logs, test output), keeping the full result available on request. One-command setup for Claude Code, Codex, Claude Desktop and Cursor.

Enables centralized proxy and orchestration of MCP servers, providing unified tool discovery, authentication, and secure request routing. It adds a human-in-the-loop approval gate for high-risk actions and audit logging.

Enables ZCode to control a real Chrome browser, including tabs, navigation, page content, clicks, form filling, keyboard input, scrolling, screenshots, and JavaScript evaluation through a local MCP bridge.

Enables declaring a catalog of tools for LLMs, filtering them by role, validating arguments, guarding sensitive calls with server-side confirmations, and recording an audit log.

Enables agents to send only the handful of tool definitions relevant to each question, ranking a large catalog by keywords and optional embeddings to cut context cost and wrong-tool picks. It can also run in front of any mcpServers config as a proxy that exposes just search_tools and call_tool instead of the whole catalog.

Routes external MCP tools behind a small on-demand discovery interface for Codex agents, using Jev to select relevant tools and providing fallback to the full inventory when confidence is low.

Enables locally configured MCP servers to be called over stdio and mixed with xAI server-side tools, without relying on hosted xAI MCP.

Enables agents to search across the pooled tools of many connected MCP servers and invoke them through a single endpoint, returning only the handful of tools relevant to each request. Filters by the caller's permissions before ranking, so agents never see or plan around tools their user is not allowed to use.

Enables AI agents to securely discover, invoke, and manage tools through a hardened MCP endpoint with protections like injection detection, circuit breakers, retry backoff, response caching, context-window limiting, and state snapshots.