"A system for semantically searching local Markdown files" matching MCP servers:

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.

A Model Context Protocol (MCP) server that provides a local-first RAG engine for your markdown documents. It uses a file-based Milvus vector database to index your notes, enabling LLMs to perform semantic search and retrieve relevant content from your local files.

An MCP server that enables users to download webpages as markdown files using r.jina.ai service, with features for configurable download directories and automatic date-stamped filenames.

Enables managing and searching markdown notes with semantic search, question answering, and note generation, and provides an MCP server for GitHub Copilot integration.

Converts any URL to clean, LLM-ready Markdown by removing ads, navigation, and other clutter, using Mozilla Readability and Turndown.

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

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.

Local-first knowledge system for reasoning agents, exposing facts, evidence, documents, retrieval, and audit history through a thin stdio MCP server.

Enables AI coding agents to search and read local Markdown knowledge bases generated by Agent Docs for Markdown, across single or multiple workspaces, including link/backlink discovery and index status checks.

Enables LibreChat agents to manage per-user private files, persistent documentation, RAG indexing and semantic search, and publish files via secure opaque public links.

Provides semantic search over markdown documentation using RAG, allowing natural language queries and integration with MCP clients.

MCP server for a modular RAG system that enables natural language question answering over enterprise documents with intent-aware routing, adaptive retrieval, and citation-backed responses.

A Model Context Protocol server that provides RAG capabilities for markdown documents using Qdrant for vector storage and Ollama for embeddings, enabling semantic search and document ingestion directly from Cursor IDE.

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

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.

About Your task manager is the best agent memory you're not using. An agent-native context layer over your existing task app.

A local MCP server that indexes codebases using Tree-sitter AST parsing and LanceDB, enabling AI models to search across repositories with sub-second latency.

A local-first MCP server for PageIndex — the vectorless, reasoning-based RAG framework. It lets local AI agents index and query local PDF and Markdown documents through a self-hosted PageIndex installation, without requiring any PageIndex cloud API key.

Local-first temporal knowledge graph with six read-only MCP retrieval tools. Supports Ollama and embedded LadybugDB; additions use a separate CLI proposal queue with human approval and explicit application.

Enables MCP clients to administer a local LM Studio instance: loading, estimating, downloading, configuring and unloading models, running chat and embedding inference, diagnosing the server, engines and hardware, and managing MCP connectors with conflict-safe configuration. It also builds a local document index supporting citation-checked search and answers grounded in TXT, Markdown, PDF and DOCX files.