"markdown" matching MCP servers:

Enables AI to search, retrieve, list notes, and get backlinks from a published Markdown vault (Obsidian) via GitHub Pages.

Headless semantic MCP server for Obsidian, Logseq, Dendron, Foam, and any markdown folder. Features built-in hybrid semantic search, surgical AST editing, template scaffolding, zero-config local embeddings, and workflow tracking.

Local-first memory server for AI coding agents that stores work sessions, tasks, and durable memories in Markdown files, exposed through MCP tools for session management and memory retrieval.

Python MCP server for programmatic access to markdown-based knowledge vaults, enabling AI assistants to browse, read, search, update, and manage notes, tasks, and projects.

Enables AI assistants to search, read, and navigate a folder of Markdown notes as a knowledge base, with full-text search, backlinks, tags, metadata, and note resources for conversation attachment.

Provides hybrid search over local markdown knowledge bases using BM25 keyword search and vector semantic search. Enables indexing, querying, and retrieving markdown documentation with dual-mode support for local and remote agents.

Enables querying Markdown files in knowledge bases (like Obsidian vaults) by front matter metadata, filtering notes by tags, recency, and folders to surface relevant content based on created/updated dates.

Enables AI assistants to query and interact with a graph database of markdown notes, extracting entities like wikilinks, mentions, and hashtags.

Enables AI agents to maintain a structured Markdown or Obsidian memory vault with tools for reading, writing, searching, and organizing notes.

Local markdown notes for any MCP client: ranked + semantic search, tags, todos, a wiki-link knowledge graph, daily notes, templates, and slash-command workflows. Pure-JS, local, no API keys.

Enables AI agents to persist all generated deliverables as structured Markdown in a project's /.aiartefact directory, with automatic frontmatter, metadata indexing, and full-text search.

Provides a persistent memory and governance layer that allows AI coding agents to query documented architecture rules and validate code against team standards. It enables agents to verify compliance across categories like security and testing before suggesting changes to ensure consistency across development sessions.

Local, searchable project memory for AI coding agents. Markdown source of truth, MCP interface, safe structured updates

Review-gated MCP sidecar for Markdown notes and incoming artifacts

Enables AI clients to read, search, write, tag, backlink, and render Markdown notes stored on a NAS through the local API of the Markdown Reader & Editor desktop app.

Persistent, local, cross-IDE memory for AI agents — markdown source of truth, LanceDB-powered semantic search, zero cloud dependency

Enables AI assistants to maintain persistent project context across sessions by storing and retrieving structured information in markdown files organized in a memory bank directory.

Captures and retrieves Markdown records for a personal recording system, enabling journaling, input tracking, search, and date-range review.

Stores AI memories as Markdown files for visualization in Obsidian's graph view, allowing users to create knowledge graphs with entities, relations, and observations.

An MCP server that enables interaction with Markdown knowledge bases, allowing users to search and retrieve content by tags, text, URL, or date range from their local markdown files.