Your agent needs better content. Here's how to give it.

Agent Context offers an advanced approach to retrieval-augmented generation (RAG) by enhancing AI agents' ability to access structured content, moving beyond the limitations of traditional text-based embeddings. It implements the Model Context Protocol (MCP) to provide AI agents with both semantic search and structured content retrieval capabilities, enabling more accurate and contextually relevant responses. This system improves upon earlier methods by allowing agents to perform precise queries using GROQ, thereby avoiding common issues like data staleness and misinterpretation of flattened content structures. The integration with Sanity's Content Lake ensures real-time updates and structured data management, making it easier for developers familiar with Sanity to build effective AI agents. Agent Context not only enhances the accuracy and functionality of AI-driven applications but also leverages existing content structures, making it an attractive option for those already using Sanity's platform.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.