"Local RAG system for providing documentation to a large language model (LLM)" matching MCP servers:

Enables a cloud orchestrator to query a local Ollama model over Tailscale or a LAN as a side-channel for drafts, summaries, and other helper output, with tools to list available models and to redact text. Answers leaving the server are passed through output-only DLP that masks card numbers, IBANs, emails, phone numbers, API keys, and similar secrets before they reach the cloud.

MCP server that lets Claude Code delegate mechanical tasks to a local LLM for summarization, classification, extraction, and drafting.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Lets AI assistants act as a personal language teacher by storing a learner's vocabulary, writing stories around those words, judging translations, labeling mistakes by rule, and scheduling spaced-repetition reviews with FSRS. It also serves the agreed tutoring method to any connected assistant so teaching stays consistent across Claude, ChatGPT and Codex.

Bridges local LLMs running in LM Studio with MCP clients like Claude Desktop to perform reasoning and analysis tasks while keeping sensitive data private. It features a suite of tools for local code review, privacy scanning, and content transformation using auto-discovered local models.

Crawl any documentation site and return heading-bounded, LLM-ready chunks with source metadata for Pinecone, Chroma, pgvector, LangChain, or LlamaIndex.

Runs a language server and provides tools for communicating with it. Language servers excel at tasks that LLMs often struggle with, such as precisely understanding types, understanding relationships, and providing accurate symbol references.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Enables household members to use Alexa+ in their own language and script, with shared lists, reminders, devices, and messages stored as spoken and rendered for each person.

Provides a testable gateway path for running a local MCP-style server in hosted-like conditions, with tenant-isolated reads, approval-denied writes, deterministic failures, and smoke-test verification.

MCP server for searching, reading, listing, writing, and appending Markdown wiki content using RAG with ChromaDB.

An MCP server that seats multiple LLMs as a council, letting your assistant query them in parallel or in sequence, relay answers for cross-critique, and merge conclusions within one conversation.

Provides full-text search and retrieval tools for Apache Spark documentation using SQLite FTS5 with BM25 ranking. It enables AI assistants to efficiently search, filter by section, and read specific Spark documentation pages.

Serves an LLM-maintained markdown wiki to agents over MCP, providing tools for querying and navigating wiki pages without direct filesystem access.

Enables access to OpenTelemetry documentation by fetching and converting pages to markdown format and searching content using Google Custom Search. Provides comprehensive documentation lookup and search capabilities for OpenTelemetry concepts, instrumentation, and configuration.

Live LLM API pricing: current token prices, model comparisons, cheapest-model lookups, and The LLM Price Index for 150+ models across 20+ providers, re-verified daily. No API key required.

Enables MCP-compatible clients to call TokenHub's image generation and editing APIs through natural language, supporting text-to-image, image-to-image, and configurable model and endpoint settings.

MCP server that provides AI assistants with real-time, current information about AI models from OpenAI, Anthropic, and Google, including pricing, capabilities, and context windows.

Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.

RAG Vault lets your AI coding assistant search your private documents locally. Everything runs on your machine and your data stays private.