czlonkowski/skills/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) | | $helpers | Full helpers incl. httpRequest | 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.

Fix: $fromAI() is a helper for other tool-enabled nodes (HTTP Request Tool, SendGrid Tool, toolWorkflow, etc.) — it's not exposed inside toolCode. Read the AI's input from query directly (or use specifyInputSchema for structured fields).

Error 2: "Wrong output type returned"

Cause: you returned a workflow-style array like [{ json: { ... } }]. That's the Code node contract, not the Code Tool contract.

Fix: return a string. For structured data, return JSON.stringify(output).

Error 3: "The response property should be a string, but it is an object"

Cause: you returned a plain object or array.

Fix: JSON.stringify() the result, or coerce to a string.

Error 4: AI never calls the tool

Cause: tool name is generic (Code Tool, My Tool) or description doesn't clearly state when to use it.

Fix: rename to a verb-y name (calculate_car_loan), and rewrite the description to explicitly state the trigger conditions (e.g. "Use this whenever the user asks about monthly cost").

Error 5: AI sends garbage into query

Cause: unstructured tool with a vague description. The LLM guesses at the format.

Fix: either (a) include a concrete JSON example in the description, or (b) switch to specifyInputSchema: true so the LLM gets a typed schema.

See: ERROR_PATTERNS.md for full catalog with reproductions.

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What's NOT Available in the Sandbox

The Code Tool sandbox is narrower than the Code node sandbox. Don't assume helpers carry over:

| Helper | Code node | Code Tool | |---|---|---| | $input.all(), $input.first(), $input.item | ✅ | ❌ | | $node["NodeName"] | ✅ | ❌ | | $json, $binary | ✅ | ❌ | | $fromAI() | ❌ | ❌ (despite sitting next to an AI agent) | | $helpers.httpRequest() | ✅ | ❌ | | DateTime (Luxon) | ✅ | ✅ (standard in JS sandbox) | | $jmespath() | ✅ | ❌ | | this.getContext(...) | ✅ | ❌ | | $getWorkflowStaticData(...) | ✅ | ❌ |

Implication: the Code Tool is for pure computation. If you need an HTTP call, an API lookup, or cross-invocation state, use a different tool node:

• HTTP Request Tool for external API calls
• toolWorkflow (Call Sub-workflow Tool) for multi-step logic with access to the full Code node sandbox
• MCP / database tools for persistent state

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When to Use Code Tool vs Alternatives

Use Code Tool when:

• ✅ Pure deterministic computation (math, parsing, formatting, validation)
• ✅ Lightweight transformations the LLM shouldn't do itself (precision math, regex)
• ✅ You want the code inline in the workflow, not in a separate sub-workflow

Use toolWorkflow (Call Sub-workflow Tool) when:

• ✅ You need multiple parameters with clean $fromAI() typing
• ✅ You need access to $helpers, credentials, or other nodes
• ✅ Logic is reusable across agents
• ✅ You want structured typed inputs WITHOUT writing a JSON Schema

Use HTTP Request Tool when:

• ✅ The tool is fundamentally a single API call
• ✅ You want per-parameter $fromAI() bindings in URL/query/body

Rule of thumb: if you find yourself wanting $fromAI(), you probably want toolWorkflow instead of toolCode.

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Complete Working Example

A production calculator tool (unstructured, JSON-in-string pattern):

{
  "parameters": {
    "name": "calculate_car_loan",
    "description": "Computes monthly car-loan payment using an annuity formula with residual/balloon. Call with a single JSON string. Example: {\"price\":439900,\"down_payment\":87980,\"interest_rate\":6.95,\"months\":36,\"residual_percent\":50,\"setup_fee\":695,\"monthly_admin_fee\":59}. Required: price, down_payment, interest_rate, months, residual_percent. Optional: setup_fee, monthly_admin_fee (default 0).",
    "language": "javaScript",
    "jsCode": "let params;\ntry {\n  params = typeof query === 'string' ? JSON.parse(query) : query;\n} catch (e) {\n  throw new Error('Invalid JSON: ' + e.message);\n}\n\nconst price           = Number(params.price);\nconst down_payment    = Number(params.down_payment);\nconst interest_rate   = Number(params.interest_rate);\nconst months          = Number(params.months);\nconst residual_percent= Number(params.residual_percent);\nconst setup_fee       = Number(params.setup_fee ?? 0) || 0;\nconst monthly_admin_fee = Number(params.monthly_admin_fee ?? 0) || 0;\n\nif (!isFinite(price) || price <= 0) throw new Error('price must be > 0');\nif (down_payment < 0 || down_payment >= price) throw new Error('down_payment must be in [0, price)');\n\nconst principal = price - down_payment;\nconst residual  = price * (residual_percent / 100);\nconst r = interest_rate / 100 / 12;\nconst growth = Math.pow(1 + r, months);\nconst base = r === 0\n  ? (principal - residual) / months\n  : (principal - residual / growth) * r / (1 - 1 / growth);\nconst monthly_payment = base + monthly_admin_fee;\n\nreturn JSON.stringify({\n  monthly_payment_sek: Math.round(monthly_payment),\n  loan_amount: Math.round(principal),\n  residual_value_sek: Math.round(residual),\n  total_cost_of_credit: Math.round(monthly_payment * months + residual + setup_fee - principal)\n});"
  },
  "type": "@n8n/n8n-nodes-langchain.toolCode",
  "typeVersion": 1.3,
  "name": "calculate_car_loan"
}

Wire it into an AI Agent via the ai_tool connection type.

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Integration with Other Skills

n8n-code-javascript: the Code node skill. Most JavaScript patterns (arrays, map/filter, DateTime) transfer — but I/O contract is different. Don't copy data-access code.

n8n-node-configuration: specifyInputSchema is a classic displayOptions-driven conditional field. Use get_node({detail: "standard"}) on @n8n/n8n-nodes-langchain.toolCode to see schema-related properties.

n8n-workflow-patterns: Code Tool sits inside the "AI Agent with tools" pattern. An agent typically has several tools; Code Tool is the "local compute" option.

n8n-validation-expert: the three Code Tool errors listed above have clear signatures — if validation surfaces "Wrong output type returned", you know to switch from array-of-items to a string.

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Quick Reference Checklist

Before deploying a Code Tool:

• [ ] Node type is @n8n/n8n-nodes-langchain.toolCode (not nodes-base.code)
• [ ] Tool name is descriptive, verb-y, snake_case (e.g. calculate_car_loan)
• [ ] Description states when to use the tool and (if unstructured) shows a JSON example
• [ ] Input read from query (JS) or _query (Python)
• [ ] No $fromAI() in the code body
• [ ] No $input / $json / $helpers — those aren't in the sandbox
• [ ] Return is a string (use JSON.stringify() for structured output)
• [ ] Wired into an AI Agent via ai_tool connection
• [ ] Tested with the exact kind of input the LLM will send (JSON in a string, or schema-validated object)

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Additional Resources

• INPUT_SCHEMA.md — structured input (DynamicStructuredTool) in depth
• ERROR_PATTERNS.md — full error catalog with causes and fixes

Official sources

• n8n Custom Code Tool docs
• ToolCode source — the sandbox contract
• LangChain tool docs — DynamicTool / DynamicStructuredTool

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Remember: the Code Tool is a LangChain tool wearing a Code-node UI. Contract is: string in, string out. Everything else follows from that.