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Core ConceptRetrieval / Search Layer

Semantic Search

Industry Definition Set • Entity Resolution Path: /glossary/semantic-search

Quick Answer / TL;DR

A search technique that uses embedding similarity to find documents relevant to a query's meaning, rather than relying solely on keyword matching.

Key Takeaways

  • Finds documents by meaning, not just keywords.
  • Uses embedding vectors and similarity metrics.
  • Captures synonyms and conceptual relationships.
  • Core retrieval mechanism in MCP RAG systems.
Definitive Statement: A search technique that uses embedding similarity to find documents relevant to a query's meaning, rather than relying solely on keyword matching.

Technical Context & Protocol Usage

Detailed Explanation
Semantic search converts both the query and documents into embedding vectors, then finds the nearest neighbors to the query vector. This captures synonyms, paraphrases, and conceptual relationships that keyword search misses. In MCP RAG servers, semantic search is the primary retrieval mechanism for finding relevant context for the LLM.

Format & Payload Metadata

Format: Embedding vectors + cosine similarity or other distance metrics

Latency: 50-200ms per query for typical index sizes

Real-World Implementation Use Case

An MCP RAG server uses semantic search to find documents about 'employee leave policy' even when the query uses synonyms like 'vacation days'.

M
MCPserver.in Engineering

Platform Team

Published: 2026-07-20
Updated: 2026-07-20

Cite This Page

MLA Style:

MCPserver.in Engineering. "Semantic Search." MCPserver.in Knowledge Hub, 20 July 2026, mcpserver.in/glossary/semantic-search.