This article provides a complete, practical walkthrough of the Splitout Redis Create Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   How to Build a Smart MCP Server with n8n and Redis to Dynamically Manage AI Workflow Tools
   
   Meta Description:  
   Learn how to build a powerful MCP server using n8n, Redis, and Subworkflow architecture to allow agents to dynamically search, add, manage, and execute workflows with input schemas – all without writing custom scripts.
   
   Keywords:  
   n8n workflow automation, MCP server, Redis memory n8n, AI agent tools, workflow discovery, automation agent, subworkflow trigger, workflow input schema, dynamic workflows, language agent tools, LangChain AI, OpenAI n8n integration, Claude Desktop integration
   
   Third-Party APIs Used:
   
   1. Redis (via n8n Redis Node) – Used for tracking the in-memory list of “available” workflows.
   2. OpenAI – Connected to an AI language model, such as GPT-4-mini, to interpret, reason, and respond intelligently during agent interactions.
   3. LangChain (via n8n LangChain + MCP Toolkit Nodes) – Integrates with custom AI agents and facilitates workflow execution.
   4. Claude Desktop (via MCP client endpoint) – Optional integration demonstrating usage of this MCP server as part of a conversational AI assistant.
   
   —
   
   Article:
   
   n8n is a powerful platform for workflow automation, ideal for chaining together diverse tasks and services without writing code. But what if you could plug your existing workflows into an intelligent AI assistant that could dynamically manage which workflows are "available" at any moment — and even choose to execute them autonomously?
   
   That’s exactly what this n8n setup does.
   
   In this guide, we’ll break down how this n8n template creates a highly-configurable MCP (Multi-Component Protocol) server using Redis and LangChain-based agents. The server enables AI assistants, like those running on Claude Desktop or custom clients, to:
   
   - Discover existing workflows,
   - Search for new workflows,
   - Dynamically update the pool of available workflows,
   - Execute workflows using parameter schemas — all without user intervention.
   
   Let’s explore how it works.
   
   🧠 Introduction: Agents Meet Automation
   
   Traditional automation tools trigger predefined workflows. This setup turns that logic on its head — the agent decides what workflows to use based on its training and context, using only those explicitly permitted.
   
   Using LangChain’s agent logic and n8n’s automation capabilities, the server acts as a bridge between human instructions and automated actions.
   
   Here’s the flow in brief:
   
   1. The AI assistant receives a task from a user via a chat interface.
   2. It searches or lists available workflows using internal tools provided by the MCP server.
   3. It adds desired workflows to a shared memory (managed with Redis).
   4. Once in memory, the agent can execute the workflow using passthrough parameters intelligently extracted from the workflow’s specification.
   
   📦 Managing Available Workflows via Redis
   
   The heart of this system is the concept of “available” workflows.
   
   Only workflows tagged with "mcp" are eligible. These workflows:
   - Must include a Subworkflow Trigger,
   - Should define input schemas (or the system extracts them),
   - Are structured and filtered to prevent redundancy or misuse.
   
   Redis stores an in-memory array of the currently available workflows. This memory updates dynamically as agents add or remove workflows, reducing clutter and human error.
   
   🛠 Tool Actions for Workflow Management
   
   The system defines 5 custom tools (i.e., workflow endpoints) to interact with workflow availability:
   
   - addWorkflow: Adds new workflow(s) to the available list by ID.
   - removeWorkflow: Removes specified workflow(s) from the pool.
   - listWorkflows: Lists workflows currently in the pool.
   - searchWorkflows: Searches the n8n instance for any applicable workflows (filtered by tags like “mcp”).
   - executeTool: Executes workflows that are already in the available pool.
   
   Each tool is represented as a subworkflow and exposed via the MCP LangChain tool node, allowing seamless and contextual use by the AI agent.
   
   🧩 Input Schema Extraction
   
   For the AI to properly execute a workflow, it must know the required parameters in advance. This is made possible through automation:
   
   - The n8n workflow’s JSON is analyzed.
   - Using JavaScript, the input schema is extracted from the Subworkflow Trigger node.
   - A simple JSON schema is generated and stored as part of the workflow metadata.
   - This schema is passed back to the agent via the listWorkflows tool.
   
   This automation means zero manual effort in documenting inputs—your workflows are “self-describing”.
   
   🤖 Intelligent Execution
   
   Once workflows are added to the available memory, the agent can:
   
   1. Validate that the workflow is available.
   2. Fetch the required input schema.
   3. Execute the workflow using passthrough parameters — avoiding hardcoded variables.
   
   Should the agent request to run a workflow not in memory, it will get an error (not crash), gracefully prompting further interaction through tools.
   
   Use cases include:
   
   - Research reports
   - Data processing
   - Sending automated emails
   - Generating summaries
   - More — limited only by your available workflows
   
   💬 Chat Agent Integration via Claude Desktop
   
   The final cherry on top? Integration with a front-end AI agent like Claude Desktop or any LangChain-compatible chat interface.
   
   The system exposes an MCP server endpoint using the @n8n/n8n-nodes-langchain.mcpTrigger node. Claude Desktop (or similar) connects via this production URL to power chats with rich, immersive automation.
   
   🤖 Summary
   
   This n8n template transforms your automation workflows into discoverable, executable tools used by a LangChain-enabled agent. Instead of chaining simple utilities, you’re chaining fully configured workflows — focusing more on outcomes than raw process steps.
   
   🎯 Why it matters:
   - Scalable: Add/remove workflows without code.
   - Smart: Uses parameter schemas + intelligent logic.
   - Secure: Limits agent access only to “approved” workflows.
   - Flexible: Integrates into any LangChain-based AI experience.
   
   💡 Tip: To extend functionality, imagine linking this to an ElasticSearch index, add user access control, or generate a custom dashboard for non-technical ops teams.
   
   This is n8n automation — supercharged with AI.
   
   — 
   Ready to try it? Tag your workflows with “mcp,” connect a Redis instance, and activate the MCP server. Within minutes, your digital assistant could be orchestrating workflows like a pro.
   
   Happy automating!

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.