Gam Seller Mcp Node MCP Server

GAM Seller MCP Node

AI buyer agents are about to participate in programmatic advertising. When they do, they need a governed interface to sell-side inventory — one that cannot be tricked into revealing sensitive data, and that cannot execute transactions it shouldn't.

This is that interface.

A governed Model Context Protocol server that exposes sell-side ad inventory (Google Ad Manager and compatible systems) to buyer-side AI agents: discovery, firm pricing, and a soft commitment primitive. It performs no writes to the ad server — the only mutation it allows is a buyer's own soft commitment (a revocable, TTL-bound intent), never a GAM order or an inventory hold. No raw ad-server access. No sensitive data in responses. Every decision audited.

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What problem does this solve?

Sell-side ad inventory (availability, pricing, product structure) lives inside ad servers that hold commercially sensitive and sometimes personal data. Giving an AI buyer agent direct API access to GAM or a similar system creates three risks:

How it works

A buyer agent connects via MCP and gets five tools — three read-only, plus a buyer-scoped commitment primitive (create/revoke) that is the sole write surface:

Buyer agent
    │
    ├── well_known_capabilities   ← Signed trust anchor. Check this first.
    │       Returns: RS256-signed capability document, node identity, privacy posture.
    │
    ├── discover_products         ← What can I buy here, and at what firm price?
    │       Returns: product families the buyer is entitled to see (e.g. "Pre-Roll Video"),
    │               each with its firm list price when the publisher has configured one.
    │       Never returns: deal IDs, internal IDs, raw inventory, exact per-impression pricing.
    │
    ├── get_forecast              ← How available is this family next quarter?
    │       Returns: Low / Mid / High availability bucket.
    │       Never returns: exact impression counts, CPM curves, floor prices.
    │
    ├── create_intent             ← Commit to a product at its current firm price (with TTL).
    │       Records a firm, time-boxed buying intent — fail-closed if the price is stale or
    │       mismatched. NOT a GAM order and NOT an inventory hold; it is the handoff artifact
    │       the classic sales rails pick up. Buyer-scoped: you can only ever commit as yourself.
    │
    └── revoke_intent             ← Withdraw one of your own active intents by id.

Every call flows through the same pipeline before any domain logic runs:

  Buyer request
       │
       ▼
  [SEC-GATE-3]  Replay detection — deduplicate client_request_id
       │
       ▼
  [Auth]        RS256 token validation → identity confirmed or AUTH_FAILED
       │
       ▼
  [Policy]      Surface denylist → entitlement check → scope check (Default-Deny)
       │
       ▼
  [Rate limit]  N=1 / T=30s per buyer_id
       │
       ▼
  [Domain]      Catalog / ForecastEngine — synthetic today, real GAM adapter in progress
       │
       ▼
  [Audit]       Append-only hash-chained ledger, buyer pseudonymized (HMAC)
       │
       ▼
  Response to buyer

A bug in any gate fails closed, not open.

create_intent runs the same gates and adds one more before it records anything: the buyer's price_ref must match the family's current firm price, or the request is rejected — fail-closed on a stale or mismatched offer, so an intent can never pin a price the publisher is no longer offering.

Quick start

Install in an MCP client (via npx)

Add the server to your MCP client (Claude Desktop, Claude Code, Cursor, …):

{
  "mcpServers": {
    "gam-seller": {
      "command": "npx",
      "args": ["-y", "gam-seller-mcp-node"]
    }
  }
}

Or run it directly (stdio transport — the default for MCP clients):

npx -y gam-seller-mcp-node

Demo mode. With no config of your own, the node boots on a bundled pilot-publisher example (illustrative catalog, prices and forecasts) and says so on stderr — it starts instead of failing, so you can try the tools immediately. Because buyer surfaces always require a token (there is no anonymous path, even in demo), the node prints a ready-to-use demo buyer token on startup: copy it and pass it as the token argument to discover_products / get_forecast to see the example families, prices and forecasts.

For a real deployment, point MCP_CONFIG_DIR at a directory holding your own deployment.json, catalog.json, entitlements.json and pricing.json:

MCP_CONFIG_DIR=/etc/gam-seller/config npx -y gam-seller-mcp-node

From source

git clone https://github.com/juan-sibbo/gam-seller-mcp-node.git
cd gam-seller-mcp-node
npm install
npm run build
npm run start:http   # HTTP transport on 127.0.0.1:3900

Run the full buyer-agent walkthrough (scripted demo):

npx tsx demo/run-demo.ts

With Docker

docker compose up

The node starts on 127.0.0.1:3900. The well-known document is at /.well-known/seller-mcp-capabilities. Persistent volumes for keys and audit data are pre-configured in docker-compose.yml.

Configure for your publisher

Four JSON files drive all publisher-specific behaviour — no code changes needed. Place them in config/ (from-source) or in the directory named by MCP_CONFIG_DIR (npx/containerised):

deployment.json     # DSR contact, controller model, data retention window
catalog.json        # product families + per-buyer access grants
entitlements.json   # which buyers are entitled to which MCP surfaces
pricing.json        # firm list prices per family (fail-closed on expiry)

Invalid config always fails closed: a malformed file stops the node rather than running with a silently different access policy. Absent config (no config/ and no MCP_CONFIG_DIR) drops to the bundled config/examples/pilot-publisher/ example — demo mode, announced on stderr — so the node is never a broken install, only ever a real deployment or a clearly-labelled demo.

Why not just use the GAM API directly?

Current status

Working MVP. The full request pipeline (auth → policy → rate-limit → domain → audit), the buyer-scoped commitment primitive (create_intent / revoke_intent, with TTL expiry), the audit ledger, GDPR data-subject-rights toolkit, Docker packaging, HTTP transport, and a live interop probe (Python buyer agent simulation) are all implemented and tested.

Not yet wired: a live Google Ad Manager connection. The catalog and forecast data are synthetic, loaded from local config. The GAM ForecastService SOAP adapter interface exists (src/forecast/source.ts) and is the next major milestone. See the open issues for the roadmap.

Architecture

See docs/ARCHITECTURE.md for the full module map and data-flow diagrams.

Key modules:

Security model

Default-Deny. Every request is denied unless an explicit entitlement says otherwise — there is no "allow by default" path in the code.

Structural allow/denylist (SEC-GATE-*). Response surfaces are governed by a fixed list enforced at the policy layer, independent of which tool was called. Exact pricing, deal IDs, raw availability numbers, cross-buyer state, real inventory holds (soft-lock), and any ad-server write are permanently denied. The one permitted write is a buyer's own commitment (create_intent / revoke_intent), which required an explicit amendment to the surface allowlist and stays buyer-scoped. Adding a new tool in the future cannot bypass this.

Opaque errors. A denied request, a failed authentication, and a revoked token all return the same generic AUTH_FAILED code. Internal reasons never reach the buyer.

Audit-first. Every allow/deny is written to the ledger before the response is sent. Buyer buyer_id values are pseudonymized (HMAC-SHA256) before entering the chain.

Privacy by construction. Responses carry only inventory-level data (product family, coarse bucket). User-level attributes don't exist in any response path.

See docs/DESIGN-PRINCIPLES.md for the full reasoning.

Testing

npm test                              # full suite (vitest)
python3 sandbox/buyer-agent-probe.py  # external Python interop probe (no shared code with server)

The test suite includes:

• Unit tests for each module (policy, pricing, identity, audit, catalog, forecast, DSR)
• Integration tests over real in-memory MCP transports (tests/server.test.ts)
• HTTP transport tests over a real ephemeral-port HTTP server (tests/http.test.ts)
• End-to-end session tests simulating a full buyer-agent session (tests/buyer-agent-session.test.ts)
• External Python probe that exercises the HTTP transport without any shared Node.js code

CI runs on every push via GitHub Actions.

Data protection

Raw buyer_id values never enter the audit ledger — only an HMAC pseudonym. The src/dsr/toolkit.ts implements export, restriction, and erasure of a buyer's audit data (GDPR Art. 15/17/18/20). The node stores nothing about end users; the DSR scope is exactly what it records — B2B buyer organization pseudonyms and their request events.

Roadmap

See the open issues for the full roadmap. Highlights:

• Real GAM adapter — wire getAvailabilityForecast via the ForecastService SOAP API
• Buyer agent SDKs — Python and TypeScript client libraries for the MCP buyer flow
• Multi-publisher federation — let buyer agents discover across multiple seller nodes
• OIDC buyer authentication — replace manual entitlements with federated identity
• OpenRTB 3.0 taxonomy — align family_id scheme with IAB standards
• Prometheus metrics — observability endpoint for production deployments

Contributing

See CONTRIBUTING.md. Issues tagged good first issue are a good starting point.

License

MIT — see LICENSE.