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tool-call-tracking

Capture tool name, summarized inputs, results, latency, and success status in structured traces.

7|1|Updated Dec 26, 2025
Search Tags:#logging#agent#latency#errors#instrument#tool-tracing
One-click install
npx skills add https://github.com/nexus-labs-automation/agent-observability --skill tool-call-tracking
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tool-call-tracking
Source: https://github.com/nexus-labs-automation/agent-observability/tree/main/skills/tool-call-tracking
Command: npx skills add https://github.com/nexus-labs-automation/agent-observability --skill tool-call-tracking

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Instrument tool executions to understand agent behavior, capturing which tool was used, with what input, what output was produced, and whether it succeeded.

Core Features & Use Cases

  • Safe logging of tool.name, tool.success, and tool.latency_ms, plus a summarized view of tool.args to avoid exposing sensitive data.
  • Capture detailed error context and result metadata to support debugging and audits.
  • Wrapper pattern for consistent instrumentation across frameworks (e.g., LangChain, CrewAI) and easy adoption by engineering teams.
  • Framework integrations and standardized error categorization to speed up incident response.
  • Provides end-to-end traceability for multi-step tool interactions in autonomous agent workflows.

Quick Start

Instrument a tool call in your agent workflow to capture its context in a tracing span.

Frequently Asked Questions about tool-call-tracking

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I trace tool executions and latency in autonomous agent workflows?▼

Instrument tool calls using a wrapper pattern to capture the tool name, safely summarized arguments, results, latency, and success status in a structured trace for debugging autonomous agent workflows.

What is the best way to log tool calls safely without exposing sensitive input arguments?▼

Capture tool name, success status, and latency while applying safe summarization to input arguments to prevent exposing sensitive data during agent debugging and audits.

Does this tool tracing approach work with agent frameworks like LangChain and CrewAI?▼

Yes, the wrapper-based integration pattern provides consistent instrumentation across frameworks like LangChain and CrewAI for easy adoption by engineering teams.

How do I capture error context and result metadata when an agent tool fails?▼

Instrument tool executions to capture detailed error context and result metadata, using standardized error categorization to support debugging and speed up incident response.

Why does my multi-step tool interaction lack end-to-end traceability for debugging?▼

It lacks traceability because tool calls are not instrumented to capture their context in tracing spans, which is necessary to provide end-to-end traceability for multi-step tool interactions in autonomous agent workflows.

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