# gqai

> gqai — fotoetienne-gqai. Use this tool when you need to generate concise descriptions for AI agents, solving problems related to search optimization and knowledge summarization. It takes in minimal input and produces a brief, informative output, making it ideal for applications where brevity and accuracy are crucial. This tool is particularly useful in contexts where AI agents require efficient and effective communication.

Canonical page: https://skillsregistry.net/skills/fotoetienne-gqai  
JSON: https://api.skillsregistry.net/v1/skills/fotoetienne-gqai

## Description

Turn any GraphQL endpoint into a set of MCP tools

## Trust

- **Trust score (0–1):** 0.81
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/aej7lu3yvt)
- **Repository:** <https://github.com/fotoetienne/gqai>

## 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": "fotoetienne-gqai"
    }
  }
}
```

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