#embeddings

A powerful Model Context Protocol (MCP) server using gemini embedding 3 that transforms any local directory into an ultrafast, visually-aware spatial search engine for AI agents.

MCP server for deAPI — 33 AI tools: transcription, image/video generation, TTS, music, OCR, embeddings via deAPI.

Enterprise-grade (40m+ lines) codebase intelligence in a zero-setup, private and local MCP: managed indexing, hybrid semantic search, polyglot code dependency graphs, and DB/API/infra knowledge. Benchmark: 61% less tokens, 84% fewer calls, 37x faster than standard AI grep.

36 enterprise-grade MCP tools for AWS infrastructure, security compliance, AI workflows, agent governance, and operational resilience. Includes IAM policy validation with security scoring, MFA compliance auditing, CloudFormation/SAM template generation, DynamoDB single-table design, OAuth configuration validation, vector embedding generation via Amazon Bedrock, error pattern analysis, data lake readiness assessment, risk classification, task routing, 12 ACI governance tools for AI agent safety (decision classification, financial gates, compliance checks, blast radius estimation, rollback generation), and 4 local stakeholder interview tools that require no API key.

An MCP (Model Context Protocol) server for indexing and querying codebases using CocoIndex. Features semantic code search with natural language queries, incremental indexing for fast updates, multi-language support (Python, JavaScript/TypeScript, Rust, Go, Java, C/C++, C#, SQL, Shell), flexible embeddings via local SentenceTransformers or 100+ cloud providers via LiteLLM, and portable SQLite storage with no external database required.

MCP server for Voyage AI embeddings, reranking, and MongoDB Atlas Vector Search. Provides 11 tools for semantic search, document ingestion, cost estimation, and model exploration. Full RAG pipeline: chunk, embed, vector search, and rerank from any MCP client.

The TwelveLabs MCP Server provides seamless integration with the TwelveLabs platform. This server enables AI assistants and applications to interact with TwelveLabs powerful video analysis capabilities through a standardized MCP interface.

A Model Context Protocol (MCP) server for agentic retrieval and semantic search over unstructured and structured data using Milvus, a high-performance vector database. This server enables large language model (LLM) applications to efficiently index, store, and retrieve vector embeddings derived from diverse data sources—such as documents, images, metadata, or logs. By leveraging Milvus, the MCP server supports similarity search and contextual retrieval, enabling intelligent access to relevant information based on natural language queries. Designed for integration with MCP-compatible clients, this solution provides a scalable, low-latency foundation for building AI-powered applications with contextual awareness and retrieval-augmented generation (RAG) capabilities.

Self-hosted MCP Japanese text indexing & search—chunking+embeddings with BM25×vector rerank

API service to search vectorized Cursor IDE chat history using LanceDB and Ollama

API service to search vectorized Cursor IDE chat history using LanceDB and Ollama

eShopLite is a set of reference .NET applications implementing an eCommerce site with features like Semantic Search, MCP, Reasoning models and more.

Branch-Thinking MCP Tool A TypeScript-powered MCP server for managing parallel branches of thought, semantic cross-references, and persistent tasks. Features dynamic scoring, AI-generated insights, batch operations, and visual graph navigation for advanced agentic workflows.

eShopLite is a set of reference .NET applications implementing an eCommerce site with features like Semantic Search, MCP, Reasoning models and more.

🦀 Prevents outdated Rust code suggestions from AI assistants. This MCP server fetches current crate docs, uses embeddings/LLMs, and provides accurate context via a tool call.

A code repository indexing tool to supercharge your LLM experience.