# Datafocus

> Use this tool when you need to retrieve data results in a natural language format, enabling effortless communication between AI assistants and data sources. Datafocus solves problems of data accessibility and interpretation by allowing direct queries and providing intuitive outputs. It is ideal for use cases where AI agents require straightforward data retrieval and processing, accepting natural language inputs and returning relevant data results.

Canonical page: https://skillsregistry.net/skills/focus  
JSON: https://api.skillsregistry.net/v1/skills/focus

## Description

A Model Context Protocol (MCP) server enables artificial intelligence assistants to directly query data results. Users obtain data results in natural language.

## Trust

- **Trust score (0–1):** 0.87
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/focus)
- **Repository:** <https://github.com/focussearch/focus_mcp_data>

## 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": "focus"
    }
  }
}
```

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