# Spanly

> Use this tool when you need to monitor and troubleshoot AI agent performance in real-time, solving issues with live traffic, errors, and duration. Spanly provides insights through querying capabilities, accepting input parameters and returning outputs such as error rates and alert notifications. Ideal for use cases requiring immediate visibility into AI agent operations, helping to identify and resolve problems promptly.

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

## Description

MCP observability. Query live traffic, errors, duration, and alerts from your AI agent.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-06-15

## Source

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

## Use it

MCP endpoint published by the skill: `https://mcp.spanly.com/`

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-spanly-spanly"
    }
  }
}
```

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