Skip to main content
Glama

get_execution_trace

Retrieve a per-node trace summary for n8n workflow executions to analyze performance and debug issues.

Instructions

Return a lightweight per-node trace summary for an execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
executionIdYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns a 'lightweight per-node trace summary', which hints at read-only behavior and output format, but lacks details on permissions, rate limits, error handling, or what 'lightweight' entails (e.g., limited fields vs. full traces). For a tool with zero annotation coverage, this is insufficient.

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?

The description is a single, efficient sentence that front-loads the core action ('return') and resource. There is no wasted wording, and it directly conveys the tool's function without unnecessary elaboration, making it highly concise and well-structured.

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

Completeness2/5

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

Given the tool's complexity (retrieving trace data), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't cover behavioral aspects like safety, output format, or error conditions, leaving significant gaps for an AI agent to understand how to use it effectively in context with siblings.

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?

The input schema has 1 parameter (executionId) with 0% description coverage, so the schema provides no semantic context. The description adds no parameter information beyond implying 'executionId' is needed for trace retrieval. It doesn't explain format, sourcing, or constraints. With low schema coverage, the description fails to compensate adequately, resulting in a baseline score.

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

Purpose4/5

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

The description clearly states the verb ('return') and resource ('lightweight per-node trace summary for an execution'), making the purpose understandable. It distinguishes from siblings like 'get_execution' (full execution details) and 'list_executions' (multiple executions) by specifying it returns trace summaries. However, it doesn't explicitly contrast with all siblings, keeping it at 4 rather than 5.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an executionId from another tool), exclusions, or comparisons to siblings like 'get_execution' or 'evaluate_workflow_result'. Without such context, users must infer usage from the name alone.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Souzix76/n8n-workflow-tester-safe'

If you have feedback or need assistance with the MCP directory API, please join our Discord server