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n8n_manage_agents

Destructive

Manage n8n AI agents end-to-end: create, mutate, validate, call, publish, revert, and delete persisted assistants with memory, tools, and channels via the MCP server.

Instructions

Manage n8n Agents (persisted assistants with a model, instructions, tools, skills, tasks, memory and channels) through n8n's instance-level MCP server. Requires N8N_MCP_ACCESS_TOKEN (MCP API key from n8n Settings → Instance-level MCP) and n8n >= 2.34 with the agents module. Actions: reference, search, get, create, mutate, validate, call, publish, unpublish, revert, versions, delete, discover_assets, verify_mcp_server, update_integration. Start with action=reference (config shape and mutate operations), then discover_assets → create → mutate (one resource at a time, always with the latest configHash) → validate. publish only when the user explicitly asks. call runs the agent with real credentials and tools and may return approvals[] that need a human decision — never approve on the user's behalf. This is not the AI Agent workflow node; use get_node for that.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
argsNoArguments for the action, forwarded to n8n verbatim. See tools_documentation("n8n_manage_agents", "full") for the per-action fields.
actionYesOperation to perform
timeoutMsNoRequest timeout in ms. Default 30000; 180000 for action=call. The agent run continues in n8n even if this expires.
Install Server

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark this as destructive and open-world, but the description adds crucial context: required N8N_MCP_ACCESS_TOKEN, n8n >= 2.34, real credentials and tools for call, and the approvals[] human-decision requirement. No contradiction with annotations exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but front-loaded with purpose and prerequisities, followed by action list, workflow, and safety caveats. The action enum is partially redundant with the schema, but each sentence contributes actionable guidance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 15 actions and no output schema, it covers auth, version requirements, invocation order, destructive and publish caveats, call behavior, and sibling differentiation. The instruction to start with reference makes the tool effectively self-documenting for remaining details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3; the description adds value by explaining action semantics (e.g., 'call runs the agent with real credentials', 'always with the latest configHash'). However, per-action args are deferred to tools_documentation, so it doesn't fully document all parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description states a specific verb and resource: 'Manage n8n Agents' and clarifies what an Agent is. It explicitly distinguishes itself from the AI Agent workflow node with 'This is not the AI Agent workflow node; use get_node for that.' This makes sibling differentiation clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides an explicit recommended sequence: reference → discover_assets → create → mutate → validate. Also gives conditional guidance such as 'publish only when the user explicitly asks' and warns against approving approvals[] on the user's behalf, plus an explicit alternative for the AI Agent node.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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