At Ryze AI, our goal was to develop a universal, protocol-driven interface for paid advertising platforms—one that supports both granular monitoring and direct control. Model Context Protocol (MCP) provides the foundation for this: an open, machine-readable architecture for real-time ad data and operations.
Below, we outline our key design principles, implementation steps, and engineering challenges in building MCP support for ad management.
Core principles
- Interoperability: MCP must support connections to any ad platform (Google, Meta, LinkedIn, TikTok) that exposes a documented API or native MCP endpoint.
- Security: All data access and campaign actions require role-based authentication and permission controls, strictly separating read-only from write operations.
- Auditability: Every data fetch, action, and automated workflow is logged with timestamps, context, and before/after snapshots.
- Extensibility: The protocol and supporting logic must adapt as platforms evolve—new metrics, fields, controls.
Engineering steps
1. Endpoint discovery & schema mapping
Begin by documenting available API endpoints and MCP-native interfaces for each target ad platform.
Build MCP adapters for authentication and data fetching:
- For Google Ads: Map campaign, creative, audience fields to an internal schema.
- For Meta Ads, LinkedIn, TikTok: Repeat mapping, handling channel-specific quirks or limitations.
Develop normalization logic to align platform-native fields with unified internal models (e.g., CPM, CPA, targeting attributes).
2. Data sync and streaming
- Implement scheduled and event-driven data polling over MCP endpoints.
- Architect incremental sync logic to catch only changes, not re-fetch when unnecessary.
- Build streaming interfaces for real-time updates (new campaigns, spend, conversions).
3. State management and automation
- Design stateful agents that consume MCP data, store current campaign status, and evaluate business logic rules.
- Configure triggers and automation primitives (e.g., "if campaign CTR < threshold, pause and alert").
- Allow human-in-the-loop interventions and overrides at any step—never allow silent fails or changes.
4. Action execution and verification
- Construct MCP command handlers to send actionable requests: budget reallocation, pause/resume, creative updates, audience modifications.
- Validate every request with preconditions—ensure platforms support requested action and confirm no conflicting updates.
- After execution, immediately update agent state and log all outcomes.
5. Reporting and diagnostics
- Build supervised reporting layers aggregating MCP stream data into historical and live dashboards.
- Engineer notification modules for alerts (pacing drift, underdelivery, anomaly detection).
- Implement CSV export and integration with external analytics/reporting tools.
6. Audit trail & compliance
- Maintain append-only action logs with full context for every automation and manual action.
- Store diff snapshots between state changes for compliance review and debugging.
- Engineer role management interfaces for access control, permission reviews, and data retention controls.
Technical challenges
- Platform fragmentation: Each ad platform's API and MCP implementation differs; uniform integration requires careful schema mapping and exception handling.
- Real-time consistency: Eventual consistency for fast campaign updates; lag can cause misaligned spend or reporting.
- Security: Strict management of API keys, tokens, and identity—plus robust permissioning to prevent accidental campaign changes.
- Scalability: The system must handle thousands of accounts/campaigns concurrently without lag, supporting asynchronous operations and retry logic.
Summary
At Ryze AI, MCP is not a wrapper but a universal protocol interface. Our integration process involves multi-platform endpoint discovery, schema normalization, incremental data sync, robust automation logic, direct stateful command execution, supervised reporting, and strict audit controls.
The result: a reliable, fully transparent technical stack for real-time paid ad management, built for extensibility and operational resilience.
This architecture enables technical teams and advanced marketers to automate, monitor, and optimize paid campaigns across any MCP-compatible platform without compromising control or traceability. All workflows are documented, configurable, and subject to ongoing review—no black box, and no reliance on manual middleware.
