N8n code tool

n8n Custom Code Tool

Expert guidance for writing code inside @n8n/n8n-nodes-langchain.toolCode — the tool an AI Agent can invoke, not the regular workflow Code node.

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⚠️ This is NOT the Code node

The Custom Code Tool looks like a Code node in the editor — same JavaScript editor, similar layout — but it is a completely different node from a different package with a different runtime contract.

	Code node	Custom Code Tool
Node type	n8n-nodes-base.code	@n8n/n8n-nodes-langchain.toolCode
Package	n8n-nodes-base	@n8n/n8n-nodes-langchain
Invoked by	Previous node (workflow flow)	AI Agent (LangChain)
Input	$input.all() — item stream	query — string or object from LLM
Return	[{json: {...}}] (items array)	A string
$fromAI()	N/A	Not available (see Errors)
HTTP helper	this.helpers.httpRequest (auth helpers blocked)	Not exposed to the tool sandbox
State	Per-run execution data	No getContext, no $getWorkflowStaticData

If you treat it like a Code node, it fails. The rest of this skill covers the Code Tool's actual contract.

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Quick Start

Minimal JavaScript Code Tool

// `query` is whatever the AI sent (a string by default)
return `You asked: ${query}`;

Minimal Python Code Tool

# `_query` is whatever the AI sent (a string by default)
return f"You asked: {_query}"

Essential Rules

1. Return a string. Numbers are auto-converted. Anything else throws "The response property should be a string, but it is an object".
2. Input variable is fixed: query (JS), _query (Python). You cannot rename it.
3. Do NOT use $fromAI() inside the Code Tool sandbox — it throws "No execution data available".
4. Do NOT use [{json: {...}}] return format — that's for Code nodes. Throws "Wrong output type returned".
5. Use a descriptive tool name (letters/numbers/underscores, v1.1+). The agent calls the tool by its name.
6. Write a precise description — the LLM decides whether to invoke the tool based on it.

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The Two Input Modes

The Code Tool has two input shapes, controlled by specifyInputSchema:

Mode 1: Unstructured (default, specifyInputSchema: false)

The AI passes a single string as query. If you need multiple fields, the AI has to stuff them into that one string and you parse them out. In practice, LLMs will happily pass a JSON string if your description tells them to.

// Parse a JSON string the AI sent
let params;
try {
  params = typeof query === 'string' ? JSON.parse(query) : query;
} catch (e) {
  throw new Error('Expected a JSON object. Parser said: ' + e.message);
}
const price = Number(params.price);
const months = Number(params.months);
// ...
return JSON.stringify({ monthly_payment: /* ... */ });

Pros: simplest to set up, one field to describe. Cons: no schema validation — if the LLM forgets a field, the tool throws at runtime.

Best for: quick prototypes, tools with one natural input (a question, a URL, a text blob).

Mode 2: Structured (specifyInputSchema: true)

The tool becomes a LangChain DynamicStructuredTool. The LLM sees a typed argument schema and passes a validated object as query. You access fields directly.

// query is now an object matching your schema
const price = query.price;
const months = query.months;
const residual_percent = query.residual_percent;

const monthly = computeAnnuity(price, months, residual_percent);
return JSON.stringify({ monthly_payment: monthly });

Schema is defined via either:

• schemaType: "fromJson" + jsonSchemaExample (n8n v≥1.3) — paste an example JSON, n8n infers the schema
• schemaType: "manual" + inputSchema — write a full JSON Schema yourself

Pros: LLM gets type hints, invalid calls rejected before your code runs, cleaner code. Cons: a little more setup; requires n8n version with schema support.

Best for: production tools with multiple typed parameters (calculators, API wrappers, anything with numeric fields the LLM tends to stringify).

See: INPUT_SCHEMA.md for complete schema setup.

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Return Format

The return value must be a string. The LLM reads it as the tool's observation.

// ✅ String
return "42";

// ✅ Number (auto-converted to string by n8n)
return 42;

// ✅ JSON-encoded structured result (recommended for rich output)
return JSON.stringify({ result: 42, currency: "SEK" });

// ❌ Raw object → "The response property should be a string, but it is an object"
return { result: 42 };

// ❌ Workflow item format → "Wrong output type returned"
return [{ json: { result: 42 } }];

// ❌ Array → "The response property should be a string, but it is an object"
return [1, 2, 3];

Best practice: JSON-stringify structured results

When your tool has more than a trivial scalar output, return a JSON string:

return JSON.stringify({
  monthly_payment_sek: 5405,
  loan_amount: 351920,
  total_cost_of_credit: 63295
});

The LLM parses JSON reliably and can pick the fields it needs to present to the user.

Error handling: the agent reads your failures

Errors don't just stop the workflow — they go back to the LLM, which usually corrects its call and retries. Use that:

// Option A: throw — n8n surfaces the message to the agent
if (!isFinite(price)) throw new Error('price must be a number, e.g. 439900');

// Option B: return an error string — agent reads it like any tool result
if (!isFinite(price)) return JSON.stringify({ error: 'price must be a number, e.g. 439900' });

Either way, write error messages for the LLM: state what was wrong and what a valid call looks like. A bare throw new Error('invalid input') wastes the retry; an instructive message usually fixes the next call.

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Tool Name and Description

These fields are NOT documentation — they are the tool contract the LLM sees. Treat them as prompt engineering.

Name

• Must match [A-Za-z0-9_]+ (v1.1+). No spaces, no hyphens, no emoji.
• Use a verb-y descriptive name: calculate_car_loan, get_weather, search_orders.
• The agent calls the tool by this name. Code Tool (the default) is useless — the agent won't know when to call it.

Description

• Explain when to use it and what to send.
• If unstructured mode, include an example of the JSON string the LLM should send.
• If structured mode, the schema speaks for itself — just describe purpose.

Unstructured example (JSON-in-string pattern):

Deterministiskt beräknar månadskostnad för billån. Anropa med EN JSON-sträng:
{"price":439900,"down_payment":87980,"interest_rate":6.95,"months":36,"residual_percent":50}
Fält: price (SEK), down_payment (SEK), interest_rate (% per år), months, residual_percent (0-99).

Structured example (schema-defined):

Deterministically computes the monthly car-loan payment given price, down payment, 
annual interest rate, term, and residual percent. Use whenever the user asks for 
monthly cost, total credit cost, or loan breakdown.

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Top Errors and Fixes

Error 1: "There was an error: 'Cannot assign to read only property \"name\" of object: Error: No execution data available'"

Cause: you called $fromAI() inside the Code Tool sandbox.