Building an MCP integration for paid ads: Ryze AI's approach

• 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.

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.

• 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.