n8n-MCP is a Model Context Protocol server that gives AI assistants structured knowledge of n8n's workflow-automation nodes, including their properties, operations, documentation, and examples. It helps developers use AI assistants to create and work with n8n workflows. The catalogue contains skills, agents, and instructions for using it.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add czlonkowski/n8n-mcp --skill n8n-agentsgit clone --depth 1 https://github.com/czlonkowski/n8n-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/czlonkowski/n8n-mcp/n8n-agents)<a href="https://agentmods.dev/skills/czlonkowski/n8n-mcp/n8n-agents"><img src="https://agentmods.dev/badge/skills/czlonkowski/n8n-mcp/n8n-agents/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/czlonkowski/n8n-mcp/n8n-agents"><img src="https://agentmods.dev/badge/skills/czlonkowski/n8n-mcp/n8n-agents.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00156 | $0.06646 |
| Opus 5 | $0.00078 | $0.03323 |
| Sonnet 5 | $0.00031 | $0.01329 |
| Haiku 4.5 | $0.00016 | $0.00665 |
Grade A, and why
n8n-agents scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
4 near-identical copies found in the catalogue:
- n8n-agents — 100% identical, 0 lines differ
- n8n-agents — 92% identical, 33 lines differ
- n8n-agents — 92% identical, 33 lines differ
- n8n-agents — 92% identical, 33 lines differ
How it starts
The opening of the file, as written. The whole thing — 313 lines — stays where its author put it; the contents beside it link to each section on GitHub.
n8n Agents
The n8n AI Agent node (@n8n/n8n-nodes-langchain.agent) is a multi-turn LLM driver with sub-nodes for the model, memory, tools, and an optional output parser. This skill is the deep guide to designing agents and the LangChain family around them. For the high-level "where an agent fits in a workflow" picture, see n8n-workflow-patterns ai_agent_workflow.md — this skill goes one level down into how to build it well.
For node-type formats: in workflow JSON the LangChain nodes use the long @n8n/n8n-nodes-langchain.* form (.agent, .lmChatOpenAi, .memoryBufferWindow, .outputParserStructured, .toolWorkflow, .toolHttpRequest, .toolCode). When you call get_node / validate_node, use the short form (nodes-langchain.agent). See n8n-mcp-tools-expert for the format rules.
Pick the right node first
Reaching for an Agent when the task is one-shot classification or extraction is the most common over-build. Decide before you wire anything:
| You need to… | Use | Why |
|---|---|---|
| Call tools, reason over multiple turns, or hold memory | AI Agent (.agent) |
The full loop: model + tools + memory + optional parser. Also a fine default when you'd rather standardize. |
| One-shot text in → text out, no tools | Basic LLM Chain (.chainLlm) |
No agent loop, easier to debug. Still accepts an outputParserStructured sub-node. |
| Route a natural-language input to one of N branches | Text Classifier (.textClassifier) |
ONE node, N output handles, downstream wires directly into each. Not Agent + Switch. |
| Pull structured fields out of free text | Information Extractor (.informationExtractor) |
Purpose-built field extraction with a schema. |
| 3-way positive/neutral/negative split | Sentiment Analysis (.sentimentAnalysis) |
Built-in branch outputs. |
| Condense a long document | Summarization Chain (.chainSummarization) |
Map-reduce summarization built in. |
| Generate an image / audio / video | The provider's native single-call node (OpenAI, Gemini, ElevenLabs…) | NEVER wrap media generation in an Agent — see "Binary and the agent boundary". |
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 313 lines · 156 tokens per session scan A 58f1425f041a
n8n-agents is a skill published in the GitHub repository czlonkowski/n8n-mcp (22,861 stars, last pushed yesterday), licensed MIT. It adds 156 tokens to every session and 6,646 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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