# ai.factori/mcp

> Use this tool when you need to analyze location-based data without requiring SQL expertise, to solve problems such as understanding audience demographics, optimizing site selection, and measuring foot traffic. It provides a user-friendly interface to search POI data, profile audiences, and score sites, offering actionable insights as output. Ideal for use cases where location intelligence is crucial, such as retail, real estate, and marketing applications.

Canonical page: https://skillsregistry.net/skills/ai-factori-mcp  
JSON: https://api.skillsregistry.net/v1/skills/ai-factori-mcp

## Description

Search POI data, profile audiences, analyze foot traffic, and score sites — no SQL needed.

## 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:** database
- **Updated:** 2026-09-28

## Source

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

## Use it

MCP endpoint published by the skill: `https://mcp.factori.ai/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": "ai-factori-mcp"
    }
  }
}
```

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