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

An MCP server that provides local, private, synchronous access to Ollama models for prompt answering, text classification, and model listing, without any file access or command execution.

A local-first document retrieval MCP server that enables AI coding tools like Codex to search private local documents via semantic search and keyword boost, supporting ingestion of PDF, DOCX, TXT, Markdown, and HTML files.

Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.

MCP server that fetches API responses and generates ready-to-use model/POJO classes in your configured language, with built-in login/refresh token handling.

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

MCP server that enables local hybrid semantic and keyword search over private PDF, DOCX, Markdown, and text documents without sending data to embedding APIs.

Enables developers to index and search their own local PDF, DOCX, Markdown, and text documents from an MCP client or terminal, combining semantic similarity with keyword matching for exact technical terms. Everything runs on the user's machine with no API key, Docker, or external database, so private documents stay private and work offline after the initial model download.

A fully local RAG MCP server for semantic code search and code intelligence, using AST-level chunking and hybrid search to pinpoint functions, classes, and APIs. No cloud, no API keys, zero setup.

A Node.js MCP server that exposes utility and sample HRMS tools over Streamable HTTP with shared bearer-token authentication, enabling local LLM gateways to call them in read-only mode.

A local MCP server that lets Claude Code and Codex delegate repository exploration and test proposals to a remote LM Studio model, while enforcing security boundaries by keeping all repository access read-only and never applying patches or running commands remotely.

A CLI tool and MCP server that turns markdown documentation into a searchable, queryable knowledge base.

Provides AI agents with local Dutch spelling and word validation using OpenTaal/hunspell, plus detailed word information from woordenlijst.org. It enables checking full texts or individual words and retrieving linguistic details for Dutch writing tasks.

Enables intelligent handling of large files through smart chunking, search with regex support, line navigation, and streaming capabilities without loading entire files into memory.

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.

Enables AI agents to identify what language text is written in, returning top matches, confidence scores, script detection, and character statistics via a pay-per-call x402 API without requiring an API key.

Semantic search and retrieval system for local documents using vector embeddings, enabling AI-powered search across your document collections with support for multiple embedding providers.

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

Enables downloading real binary files from Egnyte and uploading large files with chunked support, complementing the official Egnyte MCP connector.

Enables AI coding agents to interact with TypeScript projects through compiler-level code intelligence, providing tools for navigation, type information, diagnostics, refactoring, and semantic search.

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.