"Kotlin RAG (Retrieval-Augmented Generation) implementation resources" matching MCP servers:

Enables AI assistants to perform retrieval-augmented generation with hybrid search, reranking, multi-modal image processing, and RAG evaluation through standardized MCP tools.

An agentic AI system that orchestrates multiple specialized AI tools to perform business analytics and knowledge retrieval, allowing users to analyze data and access business information through natural language queries.

Implements a RAG workflow that integrates with any custom knowledge base and can be triggered directly from the Cursor IDE.

Connects a RAG application to open-webui using Model Context Protocol (MCP), enabling server-to-client communication for context retrieval and tool usage in remote environments through Server-Sent Events (SSE).

Enables agents to search across the pooled tools of many connected MCP servers and invoke them through a single endpoint, returning only the handful of tools relevant to each request. Filters by the caller's permissions before ranking, so agents never see or plan around tools their user is not allowed to use.

MCP server providing tools for entity extraction, query refinement, and relevance checking to build Agentic RAG applications.

An MCP proxy that embeds tool definitions and returns only the top-k semantically relevant tools per query, reducing context usage and routing calls to upstream MCP servers.

Orchestrates multi-stakeholder feature refinement and development execution workflows for AI-assisted software teams.

Enables personalized AI tutoring by allowing students to upload PDF/DOCX study materials that are processed and indexed for semantic search. Provides intelligent responses based on the student's own learning materials using RAG technology.

An open-source platform for Retrieval-Augmented Generation (RAG). Upload documents and query them ⚡

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.

An open protocol server that implements Anthropic's Model Context Protocol to enable seamless integration between LLM applications and RAG data sources using Sionic AI's Storm Platform.

Discover and connect your agents with other agents via A2A protocol. Share resources for free or fee.

Auto-captures decision context from multi-agent workflows to preserve the 'why' behind every choice. Enables task traceability, reasoning retrieval, and continuous improvement across planning and implementation sessions.

An MCP server implementation that enables interaction with the Letta API for managing agents, memory blocks, and tools in the Letta system.

A Model Context Protocol server implementation that allows AI agents to manage DNS records for domains hosted on Cloudflare by providing tools to list, create, update, and delete various types of DNS records.

Model Context Protocol (MCP) server implementation that enables Claude Desktop to interact with Google's Gemini AI models.

A durable multi-agent orchestrator for software development with explicit run graphs, checkpoint/resume capabilities, and project memory exposed through MCP resources and tools. It enables coordinated agent workflows for coding, review, repair, CI, and approval with SQLite-backed memory retrieval and pluggable research backends.

LLM Driven Trading Platform Orchestration - Strategy Design, Research & Implementation

A persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations.