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Diagnose Workflow Error

diagnose_workflow_error

Diagnose an n8n workflow error from workflow JSON and error message, returning a root cause, a minimal proposed fix (or an honest note if not fixable), and a confidence level for review before applying changes.

Instructions

Stateless diagnosis of an n8n workflow error: given the workflow JSON and error message, returns a root cause, a minimal proposed fix (or an honest 'not fixable by a workflow edit'), and a confidence level. Does not verify, deploy, or fetch anything live — pair with human or separate review before applying fixedWorkflow.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorMessageYes
workflowJsonYesThe full n8n workflow JSON object (nodes + connections).
Behavior4/5

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

With no annotations provided, the description carries full burden and handles it well: it explicitly declares statelessness, no live fetching/verification/deployment, and the honest 'not fixable' output possibility. It also warns about applying fixedWorkflow without review. This is strong behavioral disclosure absent any annotation support.

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

Conciseness5/5

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

Three sentences, zero filler. The first sentence packs purpose, inputs, and outputs. The second clarifies the stateless boundary. The third advises on safe application of the result. Every sentence earns its place with front-loaded core purpose.

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

Completeness4/5

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

For a 2-param tool with nested objects but no output schema, the description covers purpose, behavioral scope, and honest-limit disclosures well. Missing output schema means the agent can't see the return shape (rootCause/fixedWorkflow/confidence structure), but the description names the return elements in prose, partially compensating. Could add parameter format specifics, but overall adequate.

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

Parameters3/5

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

Schema coverage is 50%: errorMessage has only a minLength constraint, and workflowJson has a description ('The full n8n workflow JSON object (nodes + connections)'). The tool description adds input context but doesn't elaborate much beyond the schema. The description implies how parameters are consumed (as inputs to diagnosis) but adds limited format/semantic detail beyond what the schema states.

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?

The description uses a specific verb ('diagnose') plus resource (n8n workflow error), states the inputs (workflow JSON + error message), and clearly lists the outputs (root cause, proposed fix, confidence level). It also distinguishes from siblings by being stateless diagnosis while get_lead_score and score_conversation imply scoring/analytics tools.

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

Usage Guidelines4/5

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

The description clearly states what it does NOT do ('does not verify, deploy, or fetch anything live') and advises pairing with human or separate review. It doesn't explicitly name alternative tools for when-not-to-use, but the stateless scope and non-verification caveat provide clear contextual boundaries.

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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