"Applications or tools for creating and managing Markdown notes" matching MCP servers:

Queryable UniFi API documentation for Network, Protect, Site Manager, InnerSpace, Mobility and Carrier Fabric - endpoint search, schema drill-down and code examples in five languages. Read-only: it serves scraped docs and never connects to a controller.

Fetches a URL and returns the main content as clean Markdown, using plain HTTP when possible and headless Chromium for JavaScript-rendered or bot-protected pages.

A generic Markdown vault MCP server with FTS5 full-text search, semantic vector search, frontmatter-aware indexing, incremental reindexing, and non-markdown attachment support that exposes search, read, write, and edit tools.

Enables macOS users to read Notes' SQLite store directly for fast full-archive search and note retrieval, and to create, update, move, and delete notes through Notes.app.

MCP não oficial para consultar e administrar uma conta autenticada do Bundled Notes Web. Este servidor local expõe bundles, notas Markdown, tags/tarefas, quadros Kanban, templates personalizados, busca e Files & Photos pelo mesmo modelo Firebase usado pelo aplicativo web.

Combines GitHub releases from multiple repositories into a single product release note, with context sources for LLM synthesis.

Search and resolve MCP server packages on the mpak registry, enabling agents to discover servers, inspect tools, and check credential requirements before installation.

A small, readable MCP learning project implemented in TypeScript that uses a local JSON notebook to demonstrate core MCP features such as tools, resources, prompts, sampling, and notifications.

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

A local-only server that enables AI agents to create, read, search, and organize Apple Notes directly on macOS. It supports folder management, note formatting, and checklists using native automation to ensure data remains private and local.

Provides AI-callable tools for modernizing legacy applications, including stack detection, API contract extraction, SQL to Flyway conversion, and migration effort estimation.

Provides an in-memory notebook interface with tools to create, retrieve, list, update, and delete notes, designed to test agent behavior through realistic MCP tool interactions.

A lightweight MCP server providing common utility tools such as unit conversion, password generation, date calculation, hashing, Base64 encoding/decoding, UUID generation, text case conversion, and text statistics, with no external dependencies.

Enables inspecting local OpenAPI specification files by listing and retrieving spec details with filters and $ref resolution.

Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.

Enables natural-language interaction with Ladybug Tools for building performance simulation, 3D modeling, and data visualization, allowing agents to perform complex environmental analysis tasks without CAD software.

repo-release-tools mcp-server automates the software release process by managing version bumps, changelog generation, and releases. It streamlines repository maintenance by handling repetitive release tasks directly through your AI assistant.

MCP server that bridges Ableton Live with AI models, enabling real-time project inspection and control such as track overview, device parameters, and audio analysis.

Exposes SQL Server and Snowflake schema metadata to AI coding agents, enabling schema search, join path discovery, and stored procedure metadata retrieval without live queries.

Enables AI assistants to perform controlled Linux system administration tasks like reading logs, managing services, cron jobs, WordPress, and executing sandboxed Python code, with strict security constraints.