automatelab-n8n-mcp

Explain a failed n8n execution

execution_explain

Read-onlyIdempotent

Diagnose failed or surprising n8n executions by pasting the run JSON; get a per-node summary that flags 0-item outputs, unresolved expressions, errors, and token usage to find where items disappear.

Instructions

Diagnose a failed or surprising n8n execution. Paste the execution JSON (from the n8n UI 'Show details' or GET /executions/:id?includeData=true); returns a per-node summary highlighting nodes that returned 0 items, unresolved ={{ ... }} expressions, errors with hints, and LLM token usage. Hits the most common debugging pain point: items 'silently disappearing' between nodes. Deterministic, rule-based.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`execution`	Yes	n8n execution payload (REST `?includeData=true` shape or raw UI export). Object or JSON string.

Output Schema

TableJSON Schema

Name	Required	Description
`findings`	Yes	Per-node findings extracted from the execution payload.
`error_count`	Yes	Number of error-severity findings.
`warning_count`	Yes	Number of warning-severity findings.

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.5.1Oct 2, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds valuable behavioral context: deterministic, rule-based, returns per-node summary with specific highlighted issues (0 items, unresolved expressions, errors, LLM token usage). No contradiction with annotations.

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?

Four sentences, no waste. Purpose is front-loaded, input format and output highlights follow. Every sentence adds value.

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?

Given the simple single-parameter input, annotations covering safety, and the existence of an output schema, the description is complete. It covers input format, output content, and behavioral characteristics, leaving no gaps for correct agent usage.

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?

The schema has 100% coverage with a description for the single parameter. The tool description adds extra clarification about the expected format (UI 'Show details' or REST endpoint), which aids correct invocation beyond the schema alone.

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' and resource 'failed n8n execution', clearly distinguishing it from sibling tools like execution_list or execution_replay which are about listing or replaying executions. It states exactly what it does: returns a per-node summary with specific diagnostic details.

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 explicitly tells when to use (diagnose failed or surprising executions) and what input to provide (execution JSON from UI or REST API). It implicitly covers when not to use (if execution succeeded or is not surprising). Does not name alternatives explicitly but the context is clear given sibling tools.

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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