> ## Documentation Index > Fetch the complete documentation index at: https://agenticadvertisingorg-fix-release-bump-classification.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Media Buy Protocol > AdCP Media Buy Protocol - Unified interface for AI-powered advertising automation across all platforms. AI is buying ads. The Media Buy protocol ensures it can buy yours. This is AdCP's core advertising automation interface, providing 8 standardized tasks for managing the complete advertising lifecycle—from inventory discovery through campaign optimization. ## Protocol Access Media Buy tasks are accessible through multiple protocols: * **[MCP (Model Context Protocol)](/dist/docs/2.5.3/protocols/mcp-guide)**: Direct integration with AI assistants like Claude * **[A2A (Agent-to-Agent)](/dist/docs/2.5.3/protocols/a2a-guide)**: Complex agent workflows and collaboration All protocols provide identical functionality - choose based on your integration needs. See [Protocol Comparison](/dist/docs/2.5.3/protocols/protocol-comparison) for guidance. ## The 8 Core Media Buy Tasks The Media Buy protocol provides these essential operations: ### Discovery & Planning * **[`get_products`](/dist/docs/2.5.3/media-buy/task-reference/get_products)**: Discover advertising inventory using natural language briefs * **[`list_creative_formats`](/dist/docs/2.5.3/media-buy/task-reference/list_creative_formats)**: Understand creative requirements and specifications * **[`list_authorized_properties`](/dist/docs/2.5.3/media-buy/task-reference/list_authorized_properties)**: Verify publisher authorization and available properties ### Campaign Execution * **[`create_media_buy`](/dist/docs/2.5.3/media-buy/task-reference/create_media_buy)**: Launch advertising campaigns with complete lifecycle management * **[`update_media_buy`](/dist/docs/2.5.3/media-buy/task-reference/update_media_buy)**: Modify budgets, targeting, and campaign settings ### Creative Management * **[`list_creatives`](/dist/docs/2.5.3/media-buy/task-reference/list_creatives)**: Browse and filter creative asset libraries * **[`sync_creatives`](/dist/docs/2.5.3/media-buy/task-reference/sync_creatives)**: Upload and synchronize creative assets across platforms ### Performance Optimization * **[`get_media_buy_delivery`](/dist/docs/2.5.3/media-buy/task-reference/get_media_buy_delivery)**: Track performance metrics and campaign delivery ## Key Design Principles 1. **Protocol-Agnostic Design**: Access through MCP, A2A, or future protocols with identical functionality. 2. **Asynchronous by Design**: Operations may take seconds to days to complete. The protocol embraces pending states as normal workflow elements. **This is not a real-time protocol** - response times range from 1 second for simple lookups to days for operations requiring human approval. 3. **Human-in-the-Loop**: Publishers can require manual approval for any operation. The protocol includes comprehensive task management for human intervention. 4. **Multi-Platform Abstraction**: A unified interface that works across Google Ad Manager, Kevel, Triton Digital, and more. 5. **AI-Optimized**: Designed for AI agents to discover, negotiate, and optimize media buys autonomously. ## Key Features * **Natural Language Discovery**: Find advertising inventory using plain English briefs * **Unified Targeting**: Consistent targeting dimensions across all platforms * **Standard Formats**: Pre-defined creative specifications powered by the Standard Creative Agent * **Creative Agents**: AI-powered agents for building, validating, and previewing creatives * **Creative Flexibility**: Support for standard IAB and custom publisher formats * **Real-time Optimization**: Continuous performance monitoring and adjustment * **Human-in-the-Loop**: Optional manual approval workflows where needed * **Performance Accountability**: Built-in feedback loop that tracks publisher delivery against promises ## Response Time Expectations The Media Buy protocol is designed as a **timely but not real-time** protocol. Response times fall into four categories: * **Simple database lookups** (\~1 second): Format and creative listings * **Inference/RAG operations** (\~60 seconds): Product discovery, signal discovery with AI/LLM processing * **Reporting queries** (\~60 seconds): Delivery metrics with data aggregation * **Asynchronous operations** (minutes to days): Campaign creation/updates, creative sync, signal activation with potential human-in-the-loop approval Implementers should design for asynchronous operation and provide appropriate user feedback during processing. ## Separation of Concerns: A Collaborative Model The Media Buy protocol is built on the principle that optimizing media campaigns is a collaborative process where each party focuses on what they do best. This separation of concerns creates efficiency and better outcomes for all participants. ### The Three Roles #### 1. Publisher Role Publishers bring expertise and data to optimize campaign delivery. Their needs are simple: * **Money**: Budget to work with * **Brief**: Clear understanding of campaign goals * **Feedback**: Performance signals to know if it's working Publishers say: *"Give me money, tell me what you're trying to do, and tell me if it's working."* #### 2. Principal (Buyer) Role Principals maintain control over their brand and campaign strategy: **Upfront Controls:** * Campaign brief and objectives * Budget allocation * Targeting overlay (e.g., "must run in California", "near our stores") * Creative approval **Real-time Signals:** * Audience data * Brand safety requirements * Frequency capping rules * Performance feedback The principal focuses on high-level campaign goals while giving publishers flexibility to optimize delivery. #### 3. Orchestrator Role The orchestrator handles the technical mechanics, similar to a DSP in digital advertising: * Information synchronization between parties * Creative asset management * Frequency capping enforcement * Real-time signal processing (AXE) * Campaign state management The orchestrator enables principals to stay focused on strategy rather than implementation details. ### Why This Model Works 1. **Expertise Alignment**: Each party focuses on their strengths 2. **Clear Boundaries**: Well-defined responsibilities prevent conflicts 3. **Flexibility**: Publishers can optimize within principal constraints 4. **Scalability**: Orchestrators handle complexity behind the scenes 5. **Transparency**: Clear signals and feedback loops This collaborative approach optimizes outcomes by letting each participant do what they do best, creating a more efficient and effective advertising ecosystem. ## Accountability & Trust Framework AdCP creates a built-in feedback loop that improves marketplace quality over time through measurable performance tracking and accountability mechanisms. ### The Performance Promise Model When publishers respond to product discovery requests, they make implicit performance promises: * **Delivery Estimates**: "I can deliver 50K impressions in this package at a \$3 CPM" * **Audience Quality**: Products targeting specific demographics or behaviors * **Minimum Exposure Commitments**: Guaranteed minimum impression delivery per user * **Format Compatibility**: Supported creative specifications and requirements These aren't guarantees, but they represent realistic expectations that can be measured and tracked. ### Measurable Accountability The protocol enables systematic tracking of publisher performance against their promises: **Delivery Tracking** * Compare actual vs. estimated impression delivery * Monitor CPM accuracy against quoted prices * Track completion rates and audience quality metrics * Measure adherence to minimum exposure requirements **Quality Metrics** * Audience alignment with targeting promises * Creative format compatibility and performance * Brand safety compliance and policy adherence * Response time and operational reliability **Historical Performance** * Track publisher accuracy over time across multiple campaigns * Identify consistent over-performers and under-performers * Build reputation scores based on promise fulfillment * Enable data-driven publisher selection for future campaigns ### The Feedback Loop This creates a self-improving marketplace where performance data influences future opportunities: 1. **Discovery Quality**: Publishers who consistently deliver what they promise receive higher visibility in product discovery results 2. **Allocation Decisions**: Buyers can factor historical performance into budget allocation decisions 3. **Price Efficiency**: Accurate delivery estimates help buyers plan budgets more effectively 4. **Marketplace Evolution**: Publishers are incentivized to provide realistic estimates and deliver quality results ### Trust Building Benefits **For Buyers**: * Reduced risk through performance-based selection * More accurate campaign planning and budgeting * Higher confidence in publisher promises * Data-driven optimization opportunities **For Publishers**: * Competitive advantage through consistent performance * Higher allocation from satisfied buyers * Reputation-based pricing power * Clear incentives for operational excellence **For the Ecosystem**: * Self-regulating quality improvement * Reduced fraud and misrepresentation * More efficient allocation of advertising spend * Long-term relationship building based on performance This accountability framework transforms the advertising marketplace from a series of one-off transactions into a trust-building system that rewards performance and reliability. ## Media Buying Lifecycle The following diagram illustrates the complete lifecycle of a media buy in AdCP: Media buying lifecycle diagram showing discovery, creation, delivery, and optimization stages ## Documentation Structure ### [Task Reference](/dist/docs/2.5.3/media-buy/task-reference/) 🔗 Complete API reference for all 8 media buying operations, from product discovery and creative management to campaign creation and optimization. ### [Capability Discovery](/dist/docs/2.5.3/media-buy/capability-discovery/) 🔍 Foundation concepts including creative format specifications and property authorization. Learn about preventing unauthorized resale and understanding format requirements. ### [Product Discovery](/dist/docs/2.5.3/media-buy/product-discovery/) 📋 Natural language approach to finding inventory, including brief structure, product models, and real-world examples. ### [Media Buys](/dist/docs/2.5.3/media-buy/media-buys/) 🎯 Complete campaign lifecycle management from creation through optimization, including asynchronous operations, human-in-the-loop workflows, performance monitoring, and data-driven campaign optimization. ### [Creatives](/dist/docs/2.5.3/media-buy/creatives/) 🎨 Creative asset management including library management, asset lifecycle, and cross-platform synchronization. Works in conjunction with the [Creative Protocol](/dist/docs/2.5.3/creative/) for building and managing creative content. ### [Advanced Topics](/dist/docs/2.5.3/media-buy/advanced-topics/) 🛠️ Advanced features including targeting dimensions, security models, design rationale, and development tools. ## Getting Started Choose your path based on your role and needs: ### **For AI Agent Developers** 1. **Start with [Protocol Selection](/dist/docs/2.5.3/protocols/protocol-comparison)** - Choose MCP or A2A based on your use case 2. **Learn [Capability Discovery](/dist/docs/2.5.3/media-buy/capability-discovery/)** - Understand creative formats and property authorization 3. **Try [Product Discovery](/dist/docs/2.5.3/media-buy/product-discovery/)** - See how natural language briefs work 4. **Reference [Task Reference](/dist/docs/2.5.3/media-buy/task-reference/)** - Implement the 8 core tasks ### **For Campaign Managers** 1. **Understand the [Media Buy Lifecycle](/dist/docs/2.5.3/media-buy/media-buys/)** - Learn the complete workflow 2. **Review [Product Discovery](/dist/docs/2.5.3/media-buy/product-discovery/)** - See how to find inventory with briefs 3. **Study [Policy Compliance](/dist/docs/2.5.3/media-buy/media-buys/policy-compliance)** - Understand approval requirements 4. **Explore [Optimization & Reporting](/dist/docs/2.5.3/media-buy/media-buys/optimization-reporting)** - Learn performance management ### **For Publishers/Sales Agents** 1. **Learn [Authorized Properties](/dist/docs/2.5.3/media-buy/capability-discovery/authorized-properties)** - Understand authorization requirements 2. **Review [Creative Formats](/dist/docs/2.5.3/creative/formats)** - See supported creative specifications 3. **Study [Advanced Topics](/dist/docs/2.5.3/media-buy/advanced-topics/)** - Deep dive into technical implementation ### **For Technical Implementers** 1. **Choose your [Protocol](/dist/docs/2.5.3/protocols/protocol-comparison)** - MCP vs A2A comparison 2. **Study [Task Reference](/dist/docs/2.5.3/media-buy/task-reference/)** - Complete API documentation 3. **Review [Advanced Topics](/dist/docs/2.5.3/media-buy/advanced-topics/)** - Security, testing, and architecture 4. **Explore [Creative Management](/dist/docs/2.5.3/media-buy/creatives/)** - Asset lifecycle and synchronization The Media Buy protocol makes advertising automation accessible to AI agents while maintaining the human expertise and approval workflows that ensure quality and compliance.