# Agentic Observability

> Use this tool when you need to monitor and optimize the performance of LLM-based agents, as it provides tracing, cost tracking, and anomaly detection to identify unusual patterns and debug issues. It captures execution traces and token spend data, surfacing insights to improve agent efficiency. Ideal for debugging and optimizing agent workflows, Agentic Observability integrates with existing workflows via an npm package installation.

Canonical page: https://skillsregistry.net/skills/mdfifty50-boop-agentic-observability  
JSON: https://api.skillsregistry.net/v1/skills/mdfifty50-boop-agentic-observability

## Description

Agentic Observability provides tracing, cost tracking, and anomaly detection for LLM-based agents. It captures execution traces, monitors token spend, and surfaces unusual patterns in agent behavior for debugging and optimization. The server installs as an npm package and integrates with existing agent workflows.

## Trust

- **Trust score (0–1):** 0.90
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-28

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/mdfifty50-boop-agentic-observability)
- **Repository:** <https://github.com/mdfifty50-boop/agent-observability-mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "mdfifty50-boop-agentic-observability"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/mdfifty50-boop-agentic-observability` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/mdfifty50-boop-agentic-observability/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt