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README.md

Trillboards

The first Digital Out-of-Home (DOOH) seller agent in the AI advertising ecosystem.

5,000+ screens across the US with deterministic real-time audience intelligence from edge AI — no cookies, no probabilistic data, no guessing.

For AI Agents

Connect to our MCP server and start buying DOOH advertising programmatically:

{
  "mcpServers": {
    "trillboards": {
      "url": "https://api.trillboards.com/mcp",
      "transport": "streamable-http"
    }
  }
}

38 tools available — discover inventory, get real-time audience signals, create campaigns, and buy media. No API key needed to start (free tier included).

Quick Start

# Register as a partner (free)
# → Returns API key + MCP endpoint
curl -X POST https://api.trillboards.com/v1/partner/agent/register \
  -H "Content-Type: application/json" \
  -d '{"agent_type": "buyer", "name": "My Agent", "email": "dev@example.com"}'

Discovery Endpoints

Endpoint Description
GET /.well-known/adagents.json AdCP agent discovery
GET /llms.txt AI-readable API documentation
GET /docs/openapi.yaml OpenAPI specification index
GET /v1/partner/pricing Usage-based pricing (free tier → pay-per-use)
POST /mcp MCP server (Streamable HTTP)

For Developers

SDKs

Package Description Install
@trillboards/ads-sdk DOOH advertising SDK with MCP server npm i @trillboards/ads-sdk
@trillboards/connect Zero-friction partner onboarding npm i @trillboards/connect
@trillboards/edge-sdk On-device audience intelligence npm i @trillboards/edge-sdk

Repositories

Repo Description
dooh-agent-example Complete MCP + REST agent example
proof-of-play-verifier Independent Ed25519 proof verification
api-docs OpenAPI specs, examples, llms.txt

API Documentation

What Makes Us Different

  • Deterministic audience data — Edge AI (computer vision + audio) on every screen, not modeled estimates
  • Proof of play — Ed25519 cryptographic signatures verify every ad impression
  • AI-native — Built for agent-to-agent commerce via MCP and AdCP
  • Free tier — Start integrating immediately, pay only when you scale

Protocols

Links