mcpField
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GingerLive

GingerLive

Remote
gingerlive.ioBusiness & Marketing0Updated today

About GingerLive

GingerLive MCP Server provides current public information about GingerLive’s livestream advertising platform for brands, streamers, and their assistants. Its tools, resources, and prompts cover ad formats, network reach, campaign examples, and streamer participation.

Key features

  1. 01Company and network information
  2. 02Livestream ad format listings
  3. 03Campaign case studies
  4. 04Streamer program details
  5. 05Advertising guides and prompts

Use cases

  1. 01Review GingerLive network reach
  2. 02Compare livestream ad formats
  3. 03Research campaign examples
  4. 04Learn how streamers join the program

MCP support

The MCP server exposes read-only GingerLive data through tools, resources, and prompts. Clients can connect to its Streamable HTTP endpoint or run the stdio server locally.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://mcp.gingerlive.io/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

6
ParameterTypeRequiredDescription
intentstringNoOptional: one short sentence on what the user is trying to accomplish, e.g. 'compare ad formats for a gaming product launch in Turkey'. Used only to improve this server's coverage. Do not include names, emails or other personal data.

Details

From the project README

GingerLive MCP Server

The official Model Context Protocol server for GingerLive, 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.

What it can do

Tools
ToolReturns
get_company_overviewWhat GingerLive is: positioning, streaming platforms, network stats, third-party measurement partners, contact links
get_network_statsNetwork reach and performance: streamer count, annual unique reach, monthly hours watched, ad view-through rate
list_ad_formatsThe livestream ad formats offered to brands, with descriptions and format badges (e.g. unskippable, adblock-safe)
list_case_studiesCampaign case studies, each with a short excerpt and link
get_case_studyThe full write-up of one case study, by slug
get_streamer_program_infoHow streamers join and earn: cost, how it works, supported platforms, sign-up link

Every tool also accepts an optional intent string: one sentence on what the user is trying to do, with no personal data. The hosted server logs each tool call (tool, arguments, intent, session id, client name, country, user agent; no IP addresses) to improve what it can answer, and deletes logs after 12 months. See gingerlive.io/privacy.

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:

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

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

git clone https://github.com/gingerlive-io/gingerlive-mcp && cd gingerlive-mcp && npm install
{
  "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 stateless: each request gets a fresh server on the official MCP TypeScript SDK's web-standard Streamable HTTP transport, with no Durable Objects or other per-session storage. Every answer comes from src/data/agent-data.json, a static snapshot generated from the same source as gingerlive.io/llms.txt, so the server only ever returns information that is already public on gingerlive.io.

PathPurpose
/mcpMCP Streamable HTTP endpoint
/mcp/server-cardSEP-2127 server card (application/mcp-server-card+json)
/healthHealth check
/.well-known/mcpSEP-1960 manifest
/.well-known/mcp.jsonRegistry server card

Run it yourself

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)

Usage logging needs a D1 database on your account: npx wrangler d1 create <name>, put its name and id in wrangler.jsonc, then npx wrangler d1 migrations apply <name> --remote. For local dev, apply with --local. The stdio server does not log.

Inspect it with the MCP Inspector:

npx @modelcontextprotocol/inspector

About GingerLive

GingerLive 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.

License

MIT © Gingerlive Bilişim Teknolojileri A.Ş.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.