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gingerlive-io

GingerLive MCP Server

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README.md
# GingerLive MCP Server

**The official [Model Context Protocol](https://modelcontextprotocol.io) server for [GingerLive](https://gingerlive.io)**, the livestream advertising platform that connects brands with 1,000+ streamers on Twitch, Kick, YouTube Live and TikTok Live.

Connect it to Claude, ChatGPT, Cursor or any MCP client and your assistant can answer questions about livestream advertising using GingerLive's own up-to-date data: ad formats, network reach, campaign case studies and the streamer monetization program.

- **Endpoint (Streamable HTTP):** `https://mcp.gingerlive.io/mcp`
- **Auth:** none (public, read-only data)
- **Registry:** `io.gingerlive/mcp` in the [official MCP Registry](https://registry.modelcontextprotocol.io/v0.1/servers?search=io.gingerlive/mcp)
- **Docs:** [gingerlive.io/developers](https://gingerlive.io/developers/)

## What it can do

### Tools

| Tool | Returns |
|---|---|
| `get_company_overview` | What GingerLive is: positioning, streaming platforms, network stats, third-party measurement partners, contact links |
| `get_network_stats` | Network reach and performance: streamer count, annual unique reach, monthly hours watched, ad view-through rate |
| `list_ad_formats` | The livestream ad formats offered to brands, with descriptions and format badges (e.g. unskippable, adblock-safe) |
| `list_case_studies` | Campaign case studies, each with a short excerpt and link |
| `get_case_study` | The full write-up of one case study, by slug |
| `get_streamer_program_info` | How streamers join and earn: cost, how it works, supported platforms, sign-up link |

### Resources

- `gingerlive://company`: company facts (JSON)
- `gingerlive://guides`: resource guides on livestream advertising (JSON)
- `https://gingerlive.io/llms.txt`: the canonical llms.txt, fetched live

### Prompts

- `plan_livestream_campaign`: scope a livestream ad campaign for a brand (optional `goal`, `budget`)
- `get_started_as_streamer`: help a streamer evaluate and join the program (optional `platform`)

## Connect

**Claude (claude.ai / Desktop):** Settings → Connectors → *Add custom connector* → `https://mcp.gingerlive.io/mcp`

**Claude Code:**

```bash
claude mcp add --transport http gingerlive https://mcp.gingerlive.io/mcp
```

**Cursor, VS Code and other clients** (`mcp.json`):

```json
{
  "mcpServers": {
    "gingerlive": { "url": "https://mcp.gingerlive.io/mcp" }
  }
}
```

**Run it locally over stdio** (same tools, no network needed except the live llms.txt resource):

```bash
git clone https://github.com/gingerlive-io/gingerlive-mcp && cd gingerlive-mcp && npm install
```

```json
{
  "mcpServers": {
    "gingerlive": { "command": "npx", "args": ["tsx", "/path/to/gingerlive-mcp/src/stdio.ts"] }
  }
}
```

Or with Docker: `docker build -t gingerlive-mcp . && docker run -i --rm gingerlive-mcp`

Then ask things like *"What livestream ad formats does GingerLive offer?"*, *"Show me GingerLive's campaign case studies"* or *"How can I monetize my Kick stream?"*

## How it works

Tools, resources and prompts are registered once in `src/server.ts` and served two ways: `src/index.ts` (the hosted Cloudflare Worker) and `src/stdio.ts` (a local stdio process). The Worker is built with the [Agents SDK](https://developers.cloudflare.com/agents/) (`McpAgent`) and the official MCP TypeScript SDK. Every answer comes from `src/data/agent-data.json`, a static snapshot generated from the same source as [gingerlive.io/llms.txt](https://gingerlive.io/llms.txt), so the server only ever returns information that is already public on gingerlive.io.

| Path | Purpose |
|---|---|
| `/mcp` | MCP Streamable HTTP endpoint |
| `/health` | Health check |
| `/.well-known/mcp` | SEP-1960 manifest |
| `/.well-known/mcp.json` | Registry server card |

## Run it yourself

```bash
npm install
npm run stdio    # local stdio server
npm run dev      # local Worker at http://localhost:8787/mcp
npm run deploy   # to your own Cloudflare account (change the route in wrangler.jsonc first)
```

Inspect it with the [MCP Inspector](https://github.com/modelcontextprotocol/inspector):

```bash
npx @modelcontextprotocol/inspector
```

## About GingerLive

[GingerLive](https://gingerlive.io) is a livestream advertising platform. Its Streamsense AI places non-intrusive, unskippable ads at the right live moment, so streamers earn from their content and brands reach Gen Z at scale.

- Brands: [gingerlive.io/brands](https://gingerlive.io/brands/)
- Streamers: [gingerlive.io/streamers](https://gingerlive.io/streamers/)
- Case studies: [gingerlive.io/casestudies](https://gingerlive.io/casestudies/)
- Contact: [info@gingerlive.io](mailto:info@gingerlive.io)

## License

[MIT](LICENSE) © Gingerlive Bilişim Teknolojileri A.Ş.

TDQS

A4.1/5.0

Scored across 6 tools

Disambiguation4/5

Each tool targets a distinct resource: company overview, network stats, ad formats, case studies, and streamer program info. There is minor overlap between get_company_overview and get_network_stats since both touch network statistics, but their descriptions clarify that one is the positioning summary and the other is the detailed metrics.

Naming Consistency5/5

Tool names consistently follow a verb_noun pattern in snake_case: get_ for single resources and list_ for collections. The naming is predictable and makes it easy to infer each tool's purpose.

Tool Count5/5

Six tools is well-scoped for an informational media-kit server. Each tool covers a distinct piece of content without redundant or excessive surface area.

Completeness5/5

The set provides full coverage of GingerLive's public information needs: company positioning, audience metrics, ad products, case study discovery with detail retrieval, and streamer monetization. The list_case_studies/get_case_study pair completes the standard list-and-reterieve workflow.

Maintenance

ActivityMaintained
ResponsivenessNo issues