Gracenote launches MCP Server to verify AI responses for TV platforms

Gracenote announced on September 3, 2025, the launch of its Video Model Context Protocol (MCP) Server, marking a significant advancement in how television platforms handle AI-powered content discovery. The product addresses critical limitations in large language model responses by connecting LLMs to Gracenote's continuously updated entertainment database.

The new system enables TV platforms to deliver conversational search capabilities while mitigating the "hallucination" phenomenon where LLMs generate inaccurate but plausible responses. According to Tyler Bell, SVP of Product at Gracenote, the company's content data and identifiers have long served as the source of truth for the global entertainment industry.

Dynamic connectivity to any LLM distinguishes the Gracenote Video MCP Server from traditional approaches. The product verifies responses in real-time using editorially-vetted entertainment data covering 40 million titles across 260 streaming catalogs in 70 languages and 80 countries. This validation process ensures platforms return accurate information based on user search inputs and discovery preferences.

The announcement comes as streaming platforms grapple with the complexity of content discovery across fragmented ecosystems. Netflix's advertising business is expected to roughly double as the platform develops proprietary technology infrastructure. Similar trends across the industry demonstrate the growing importance of sophisticated content management systems.

TV platforms can now answer complex queries through the new system, including episode-specific searches within series, award-related content discovery, and real-time sports availability. The technology processes requests such as identifying Brooklyn Nine-Nine episodes containing specific references, listing highest-grossing Oscar winners from the past decade, or locating live sports broadcasts.

Enhanced responses include related program imagery, availability information, and standardized Gracenote content identifiers. These enrichments enable data harmonization across different sources while providing linkage to related content including reviews, trailers, and ratings. The comprehensive approach addresses long-standing challenges in cross-platform content discovery.

Technical capabilities extend beyond basic search functionality. The Video MCP Server enables advanced conversational search, highly personalized recommendations, and compelling discovery journeys leveraging human-verified TV, movie, and sports data. This approach contrasts with purely algorithmic solutions that may lack editorial oversight and accuracy verification.

The product launch represents the first offering from Gracenote's new suite of AI-enabled solutions developed for breakthrough content search and discovery capabilities. As consumers increasingly rely on AI across daily activities, Gracenote's human-verified data serves as the foundation for entertainment experiences across video, sports, and music platforms.

Platform enthusiasm for AI-driven search and discovery stems from potential improvements in user engagement and monetization opportunities. LLMs provide the ability to create customized content catalogs drawing from extensive training material while enabling semantic search capabilities that can rank results against virtually any viewer criteria.

However, LLM limitations create significant challenges for platform operators. Beyond hallucination issues, these models are constrained by training data cutoff dates, meaning their knowledge only extends to specific points in time. They also lack access to visual assets tied to structured data for TV shows and movies, creating gaps in comprehensive content presentation.

The broader connected TV advertising landscape continues evolving rapidly. Magnite recently rolled out pause advertising formats across major streaming providers, while Disney launched certification programs for live streaming advertising enabling real-time bidding capabilities.

Gracenote's MCP Server addresses the critical intersection of AI capabilities and content accuracy requirements. The system maintains the conversational benefits of LLM interfaces while grounding responses in verified entertainment data. This approach enables platforms to offer advanced search functionality without sacrificing accuracy or user trust.

The entertainment industry's adoption of AI technologies accelerates as platforms seek competitive advantages in user experience and advertising capabilities. IPG Mediabrands launched contextual CTV solutions showing 2x awareness lift, 3x ad recall, and 5x favorability compared to standard CTV benchmarks.

Implementation flexibility allows the Video MCP Server to integrate with existing platform architectures. The system dynamically connects to any LLM while providing standardized content identifiers that facilitate cross-platform integration and measurement. This technical approach reduces implementation barriers while maximizing compatibility across diverse technology stacks.

Revenue implications for streaming platforms extend beyond improved user experience. Enhanced search and discovery capabilities typically correlate with increased engagement metrics, which directly impact advertising inventory value. The ability to provide accurate, comprehensive responses to user queries can reduce content discovery friction and increase overall platform usage.

Microsoft research indicates customer service roles face significant AI transformation potential, with 98% of interpreter and translator activities showing AI applicability. The Gracenote solution addresses this trend by providing technology that enhances rather than replaces human editorial oversight in content curation.

Data integration capabilities encompass multiple entertainment categories beyond traditional television content. The system covers sports programming, movie catalogs, and streaming service availability, providing comprehensive coverage for platforms managing diverse content portfolios. This breadth enables unified search experiences across different content types and sources.

The Model Context Protocol Server launches during a period of intense competition among content platforms for viewer attention and advertiser investment. AdRoll's recent brand unification demonstrated how companies are leveraging AI automation to simplify complex marketing operations while maintaining performance standards.

Quality assurance mechanisms built into the Video MCP Server ensure response accuracy through continuous validation against Gracenote's editorial standards. This approach differs from automated systems that may lack human oversight and quality control processes. The combination of AI efficiency with human verification creates a hybrid model addressing both performance and accuracy requirements.

Future applications for the technology extend beyond current search and discovery use cases. Gracenote's position as the content data business unit of Nielsen provides access to broader entertainment industry insights and measurement capabilities. This foundation enables potential expansion into analytics, recommendation optimization, and cross-platform measurement solutions.

The September 3, 2025 announcement positions Gracenote at the forefront of AI-powered entertainment technology while maintaining its traditional role as an authoritative data provider. The company offers a free whitepaper detailing the Video MCP Server's capabilities and implementation approaches for interested platforms and developers.

Timeline

• September 3, 2025: Gracenote announces Video MCP Server launch for TV platforms
• August 26, 2025: Magnite rolls out pause ads for streaming TV with DIRECTV and Fubo
• August 4, 2025: Microsoft study shows AI impacts 75% of major occupations in marketing communications
• July 27, 2025: McKinsey AI agents reshape advertising landscapeanalysis published
• July 18, 2025: Netflix declares ads business will "roughly double" as upfront deals close
• July 15, 2025: Paramount Australia partners with Magnite for programmatic streaming ads
• July 12, 2025: Disney and ITV launch content exchange across streaming platforms
• July 9, 2025: StackAdapt launches AI assistant Ivy for programmatic advertising
• July 8, 2025: MNTN partners with ZoomInfo to bring B2B advertising to connected TV
• June 16, 2025: Snowflake and Acxiom announce plans to transform AI marketing infrastructure
• January 7, 2025: Disney launches certification for live streaming ads
• January 1, 2025: New model for AI advertising emerges as agents could replace human attention

Summary

Who: Gracenote, the content data business unit of Nielsen, announced the launch through Tyler Bell, SVP of Product at Gracenote.

What: The Gracenote Video Model Context Protocol (MCP) Server connects large language models to Gracenote's entertainment database, validating and enriching LLM responses with accurate content data covering 40 million titles across 260 streaming catalogs.

When: The announcement occurred on September 3, 2025, with initial rollout targeting CTV platforms and apps.

Where: The product launches globally, enabling TV platforms to integrate with Gracenote's knowledge base spanning 70 languages and 80 countries.

Why: The Video MCP Server addresses critical LLM limitations including hallucinations, outdated training data, and lack of visual assets, enabling platforms to provide accurate conversational search while improving user engagement and monetization opportunities.

PPC Land explains

Large Language Models (LLMs): Artificial intelligence systems trained on vast amounts of text data to understand and generate human-like responses. These models power conversational search capabilities but face significant limitations in entertainment applications, including the tendency to generate plausible but inaccurate information about TV shows, movies, and sports content. LLMs are constrained by their training data cutoff dates and lack access to real-time information or visual assets tied to entertainment content.

Model Context Protocol (MCP): A technical framework that enables large language models to connect with external data sources and knowledge bases in real-time. The protocol allows AI systems to verify and enrich their responses using authoritative data rather than relying solely on training information. Gracenote's implementation of MCP represents the first entertainment-specific application of this technology for content discovery platforms.

Connected TV (CTV): Television content delivered through internet-connected devices rather than traditional broadcast or cable systems. This technology enables targeted advertising capabilities similar to digital platforms while maintaining the premium viewing environment of television. Connected TV has emerged as the fastest-growing segment in digital advertising, with streaming platforms increasingly replacing traditional television viewing habits.

Content Discovery: The process through which viewers find and access entertainment content across streaming platforms and traditional television services. Modern content discovery involves sophisticated search algorithms, recommendation engines, and user interface design that help audiences navigate vast content libraries. The complexity of content discovery has increased dramatically as streaming services proliferate and content catalogs expand.

Hallucination: A phenomenon in artificial intelligence where language models generate confident but factually incorrect responses to user queries. This occurs when AI systems create plausible-sounding information that appears authoritative but lacks basis in actual data. In entertainment contexts, hallucination can result in incorrect show information, non-existent episode details, or inaccurate availability data across streaming platforms.

Real-time Verification: The process of immediately checking AI-generated responses against authoritative data sources to ensure accuracy before presenting information to users. This capability distinguishes advanced AI systems from basic chatbots by providing dynamic fact-checking and correction mechanisms. Real-time verification becomes especially critical in entertainment applications where content availability, scheduling, and metadata change frequently across platforms.

Streaming Platforms: Digital services that deliver video content over internet connections to connected devices, encompassing subscription services, ad-supported offerings, and hybrid models. Major platforms include Netflix, Disney+, Amazon Prime Video, and numerous others that collectively represent a fundamental shift in how audiences consume entertainment content. These platforms compete intensely for viewer attention and advertiser investment while managing complex content licensing and distribution relationships.

Content Metadata: Structured information that describes entertainment content including titles, genres, cast information, release dates, episode details, and availability across different platforms. High-quality metadata enables sophisticated search and discovery capabilities while supporting advertising targeting and content recommendation algorithms. Gracenote specializes in providing comprehensive, human-verified metadata that serves as the foundation for entertainment industry operations.

Programmatic Advertising: Automated buying and selling of digital advertising inventory through real-time bidding systems and algorithmic optimization. This technology enables precise targeting, efficient budget allocation, and real-time campaign optimization across multiple channels and formats. In connected TV environments, programmatic advertising allows brands to reach specific audiences while streaming platforms maximize revenue from their advertising inventory.

AI-powered Search: Advanced search capabilities that use artificial intelligence to understand natural language queries and provide contextually relevant results. Unlike traditional keyword-based search systems, AI-powered search can interpret complex user intentions and provide conversational responses. In entertainment contexts, this technology enables viewers to find content using descriptive phrases, plot details, or subjective criteria rather than exact titles or metadata fields.