# Bling-bag

> Use this tool when you need to manage cross-platform identity and visual styling for AI agents across various surfaces, including Claude Code, desktop, terminal, web, and popular chat platforms. The Bling-bag MCP server delivers a unified styling solution through a single bling.json file, streamlining agent customization. It solves the problem of inconsistent agent branding and identity across different platforms, and is ideal for use cases requiring randomized identities and rarity-based styling.

Canonical page: https://skillsregistry.net/skills/tjclaude88-mcp-bling  
JSON: https://api.skillsregistry.net/v1/skills/tjclaude88-mcp-bling

## Description

MCP server that delivers cross-platform identity and visual styling for AI agents — one bling.json, many surfaces (Claude Code, Claude Desktop, terminal, web, Slack, Discord, IDE). Includes WOW (Weird Office Workers), a random-identity generator with 13 weighted trait pools (465 entries), five rarity tiers calibrated against a 10k-roll empirical distribution, and HOMUNCULUS-framed share

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

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

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/kxgqrjty3a)
- **Repository:** <https://github.com/tjclaude88/mcp-bling>

## 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": "tjclaude88-mcp-bling"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/tjclaude88-mcp-bling` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/tjclaude88-mcp-bling/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