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

Enables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.

MCP server that downloads and analyzes sources of Maven-published Kotlin/Java libraries, exposing structured API information, KDoc, and raw source to AI agents.

Enables deterministic auditing of RAG retrieval faithfulness and hallucination scores, providing structured JSON output for AI agents and MCP-compliant clients.

A Model Context Protocol server that enables Claude to execute Python code using boto3 to query and manage AWS resources directly from conversations.

An evaluation harness that probes MCP-based retrieval servers for calibration, relevance, coverage, citation integrity, and more, also exposing the probes as MCP tools for assistants and CI agents.

Enables WordPress content management through the Model Context Protocol, allowing AI agents to create, read, update, and delete posts, metadata, taxonomies, and terms, as well as interact with Gutenberg blocks.

A minimal MCP server with a sample search_news skill that demonstrates how Claude Code can automatically call a skill over the MCP protocol.

An open-source MCP server providing AI agents with neural web search via Exa and tiered web fetch (Exa, local browser, Firecrawl) as a drop-in replacement for built-in web tools, preserving provenance and guarding against SSRF.

Enables MCP-capable agents to perform reliable web search and content fetching through a single server that automatically fails over across multiple providers. It handles keyed or keyless authentication automatically and returns clean Markdown or text results.

Enables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).

Enables image and video generation using BigModel AI's CogView and CogVideoX models via MCP, supporting batch image generation and various configuration options.

A Model Context Protocol server that analyzes webpage design structures, providing detailed layout, navigation, content, form, image, and link information from a given URL.

A modular RAG service framework that exposes tools like query_knowledge_hub, list_collections, and get_document_summary via MCP protocol, enabling AI assistants to perform retrieval-augmented generation.

Enables document ingestion, semantic search, and retrieval-augmented generation via MCP tools and REST API, using vector embeddings and intelligent chunking.

Enables MCP-capable agents to generate and edit images through Gemini or OpenAI, returning an absolute file path instead of image bytes to keep context windows clean.

An agentic Retrieval-Augmented Generation (RAG) system that combines a small curated machine learning knowledge base with real-time web search capabilities, powered by the Model Context Protocol (MCP).

Enables WordPress content management (posts, metadata, taxonomies, Gutenberg blocks) via the Model Context Protocol, demonstrating MCP Adapter integration patterns.

Enables retrieval-augmented generation search and chat over documents stored in Azure Blob Storage, with a Streamlit app for uploading files and interacting via a LangGraph agent.

Exposes a RAG agent built with LangGraph, enabling retrieval-augmented question answering over a PostgreSQL/pgvector corpus via the MCP protocol.

Enables semantic search and question-answering over FAQ documents using RAG (Retrieval-Augmented Generation) with OpenAI embeddings and in-memory vector similarity.