"Semantic UI" matching MCP servers:

Enables AI agents to browse, search, and read a curated library of 198 UI/UX and animation design skills across 17 categories, distilled from 10 open-source projects, with a progressive 5-step workflow for guidance.

Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.

Reduces token consumption by over 80% through intelligent file caching, returning only diffs for modified files and suppressing unchanged content. It features a suite of 12 tools for semantic search, batch reading, and efficient file editing to optimize LLM interactions with large codebases.

A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.

Enables AI agents to perform keyword, semantic, hybrid, and SQL retrieval over data stored on object storage or local paths, using a local embedding model without requiring an API key.

An MCP server that enables searching Merill's Weekly Microsoft AI Roundup archive with natural language queries, returning sourced excerpts, issue details, and tool mentions. It supports hybrid semantic and keyword search, issue retrieval, and archive browsing with no API keys required.

Local semantic recall MCP server for citation-integrity workflows. Enables semantic search over a text corpus, returning file paths and page numbers for verification, with 100% local processing using ONNX embeddings and LanceDB.

Enables AI agents to query verified-emissions knowledge graphs in natural language and validate data against supported standards, with metered billing and optional verified identity.

Provides persistent, searchable memory across AI coding agent and chat history (Claude Code, Codex, Gemini CLI, ChatGPT, and more) via retrieval-augmented generation, enabling semantic and hybrid search to retain context across sessions.

Enables semantic search over local markdown specs, RFCs, and docs using Ollama and LanceDB, allowing AI agents to find relevant information by meaning rather than exact keywords.

A semantic search system for Claude Code transcript history, exposed as an MCP server so Claude Code can query its own past sessions.

Unified MCP server combining hybrid search (vector + BM25 + code graph), structural code analysis, and persistent semantic memory. 15 tools, 25+ languages, <350MB RAM, fully local.

Provides access to Jina AI's suite of web tools including URL reading, web/image/academic search, content extraction, embeddings, and reranking capabilities. Enables AI assistants to search the web, extract content from URLs, and process information with semantic understanding.

Provides access to Jina AI's web tools including URL-to-markdown conversion, web/image/academic search, screenshot capture, document reranking, and semantic deduplication. Works with optional API key for enhanced rate limits and full feature access.

A read-only PostgreSQL MCP server for AI coding agents that exposes database schema and sample data as tools, with LLM-powered semantic search enriched by a user-authored semantic layer.

Read-only semantic retrieval for agents that need to find the right Obsidian note without write access.

A self-hosted semantic code retrieval MCP server that enables natural language queries to find relevant code snippets using Tree-sitter AST parsing, embedding, and hybrid search with reranking.

Enables AI agents to store, retrieve, and connect information in a Neo4j graph database as persistent memory, with semantic relationships, natural language search, and temporal tracking across conversations.

Enables coding agents to search locally indexed repositories with hybrid semantic and lexical retrieval, returning exact source citations with file paths and line ranges.

Enables AI agents to interact with the AI-Archive platform for research paper discovery through semantic search, paper submission and management, peer review with structured scoring, and citation generation in multiple formats.