In the Modern SEO landscape of 2026, “keywords” are dead. We now optimize for Context Vectors. And context comes from three distinct protocols: MCP (Model Context Protocol), WebMCP (Web Model Context Protocol), and the emerging UCP (User Context Protocol).

Understanding the difference is the key to mastering Vector Search Optimization.

1. MCP: The Backend Context

MCP is about high-fidelity, server-side data connections. It connects an Agent directly to a database, a file system, or an internal API.

The Mathematics of Semantic Chunking: Optimizing Retrieval Density

In the frantic gold rush of 2024 to build Retrieval-Augmented Generation (RAG) applications, we committed a collective sin of optimization. We obsessed over the model (GPT-4 vs. Claude 3.5), we obsessed over the vector database (Pinecone vs. Weaviate), and we obsessed over the prompt.

But we ignored the input.

Most RAG pipelines today still rely on a primitive, brute-force method of data ingestion: Fixed-Size Chunking. We take a document, we slice it every 512 tokens, we add a 50-token overlap, and we pray that we didn’t cut a critical sentence in half.

Cosine Similarity is the core metric of the new search. It measures the cosine of the angle between two vectors in a multi-dimensional space. In the era of Answer Engines, it determines if your content is “relevant” enough to be retrieved for the user’s query.

If your content vector is orthogonal (90°) to the query vector, you are invisible. If it is parallel (0°), you are the answer.

The Math of Relevance

• 1.0: Identical meaning. The vectors point in the exact same direction.
• 0.0: Orthogonal (unrelated). The vectors are at 90 degrees.
• -1.0: Opposite meaning.

Your goal is not “keyword density” but “cosine proximity.” You want your content vector to sit as close as possible to the Intent Vector, not just the Query Vector.

SEO used to be about “Keywords.” Now it is about “Vectors.” But what does that mean?

In the Agentic Web, search engines don’t just match strings (“shoes” == “shoes”). They match concepts in a high-dimensional geometric space.

The Vector Space

Imagine a 3D graph (X, Y, Z).

• “King” is at coordinate [1, 1, 1].
• “Queen” is at [1, 1, 0.9]. (Very close distance).
• “Apple” is at [9, 9, 9]. (Far away).

Modern LLMs use thousands of dimensions (e.g., OpenAI’s text-embedding-3 uses 1536 dimensions). Every product description, blog post, or review you write is turned into a single coordinate in this massive hyper-space.