# Semantic search

Kolada MCP Server is an intelligent middleware that connects large language models with the Swedish Kolada statistical database, providing functions such as semantic search, KPI analysis, and multi - year comparison, simplifying the query and interpretation of public - sector data.

The MCP-RAG Server is an implementation based on the Model Context Protocol (MCP), aiming to provide real-time document access to AI assistants through semantic vector search, thereby generating context-aware responses based on the latest information.

The mem0 MCP Server provides structured memory storage and semantic search services for project management

An MCP server based on Claude for document management and semantic search using Needle

This is an MCP server designed for the Smart Connections plugin, providing read-only semantic search functionality for the Obsidian knowledge base, emphasizing security and lightweight implementation.

Criterion is an AI - based Islamic knowledge assistant that provides authoritative guidance through the Quran and Hadith. It uses semantic search and real - time streaming response technology, is permanently free, and focuses on core doctrines.

Engram is an event - sourcing memory system designed for AI agents, adopting an architecture without an LLM write path, and achieving reliable semantic search memory storage through local vector embeddings and DuckDB.

A local server based on the MCP protocol that implements semantic search and RAG functions for Apple Notes, which can be called by AI assistants such as Claude.

This project is a semantic search tool based on Elasticsearch for semantic retrieval of Search Labs blog posts, including crawler configuration, index mapping updates, and MCP server integration functions.

An MCP server that connects AI assistants with HubSpot CRM data, providing access to contact, company, and interaction data. Built - in vector storage and caching mechanisms optimize performance.

mc-mcp is an MCP server designed for the metacontract framework, providing support for smart contract development, including semantic document search, Foundry integration, and contract deployment.

A system integrating the Qdrant vector database and MCP server for storing and retrieving code snippets, supporting natural language search and semantic retrieval.

A semantic search MCP service based on the Qdrant vector database, supporting multi - collection retrieval and configurable result return

This project is a semantic search tool based on Elasticsearch for semantic retrieval of Search Labs blog posts, including crawler configuration, index mapping update, and MCP server integration functions.

A Machine Control Protocol (MCP) server based on the Qdrant vector database, used for storing and retrieving text information, supporting semantic search and FastEmbed integration.

A TypeScript-based MCP server that integrates with Mem0 to provide memory stream functions

CodeFlow is an intelligent code analysis tool that generates detailed call graphs by parsing the abstract syntax tree of the code and provides semantic search functionality. It includes two interfaces, a CLI tool and an MCP server, aiming to help developers understand complex codebases with minimal cognitive burden.

This is an MCP service that provides a free public API directory, supports semantic search and detailed API information queries, and is built on a public API dataset.

Claude Context is an MCP plugin that provides in - depth context of the entire codebase for AI programming assistants through semantic code search. It supports multiple embedding models and vector databases to achieve efficient code retrieval.

A high-performance MCP server that provides persistent multimodal context storage for LLM agents, supports thread isolation, metadata filtering, full-text search, and semantic search, and is compatible with SQLite and PostgreSQL backends.

This is an MCP server for Microsoft Dynamics 365 Finance & Operations development. It accesses over 50,000 F&O standard components through semantic search, enabling AI assistants to find relevant implementation patterns and read local source code during development.

Memory MCP is an MCP server that provides persistent memory for AI assistants. Through a two - tier architecture of hot cache and cold storage, it enables zero - latency automatic injection of high - frequency knowledge and semantic search, allowing Claude to remember project contexts and reduce repeated explanations.

This is a knowledge base management tool based on the MCP server, which provides a list of knowledge base content and retrieval functions, and supports semantic search and automatic index updates.

DeepContext is an MCP server that provides symbol - aware semantic search capabilities for code assistants. By intelligently parsing code structure and semantic relationships, it can accurately locate relevant code snippets in large codebases and improve development efficiency.

The Vector Memory MCP Server provides long-term memory functions for AI assistants, supports saving and retrieving files or free-form notes, and enables natural language information queries through semantic search.

The Smart Coding MCP server provides a semantic - based code search function for coding assistants through a local AI model, supporting multi - project management and progressive indexing.

DMCP is a semantic tool discovery protocol that solves the problem of too many tools in MCP servers. It realizes on-demand query-driven tool discovery through vector search, significantly reducing token consumption and improving the accuracy of tool selection.

DeepZotero is a tool that provides semantic search functionality for the Zotero literature library. It extracts text, tables, and images from PDFs, chunks, embeds, and stores them in ChromaDB. Finally, it provides 13 types of semantic search, Boolean search, table/image search, context expansion, citation graph query, index management, and cost - tracking tools to clients such as Claude Code through the MCP server.

A Python - based service application that provides functions for getting metadata from SharePoint and retrieving entities from the Neo4j knowledge graph through semantic similarity search.

A Machine Control Protocol (MCP) server based on the Qdrant vector database, supporting text storage, semantic search, and integration of the FastEmbed embedding model.