# com.argosvix/server

> Use this tool when you need to monitor and optimize the performance of large language models (LLMs) by querying costs, errors, and latency, and managing alerts and evaluations from Claude and Cursor. It provides a centralized observability platform for model performance monitoring and alert management. Ideal for use cases requiring real-time model performance insights and automated alerting.

Canonical page: https://skillsregistry.net/skills/com-argosvix-server  
JSON: https://api.skillsregistry.net/v1/skills/com-argosvix-server

## Description

Observability MCP server: query LLM cost/errors/latency & operate alerts/evals from Claude/Cursor

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-09-03

## Facts

- **Version:** 0.30.0-alpha.2
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-03

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/com.argosvix%2Fserver)

## 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": "com-argosvix-server"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/com-argosvix-server` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/com-argosvix-server/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