# com.everlange/mcp

> com.everlange/mcp — com-everlange-mcp. Use this tool when you need to audit and optimize answer engines, such as AEO, across multiple criteria to identify and fix issues, and measure the resulting improvements. It takes in answer engine outputs and returns a comprehensive audit report with suggested fixes, enabling data-driven decision making. Ideal for use cases where answer engine accuracy and reliability are critical, such as in knowledge management and question-answering systems.

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

## Description

Everlange: audit AEO on 67 criteria across 10 answer engines, apply fixes, prove the gain.

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-09-02

## Source

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

## Use it

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

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-everlange-mcp"
    }
  }
}
```

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