"Detecting Patterns and Conformations in Trading Charts" matching MCP servers:

A TypeScript-based MCP server that enables users to query financial news, stock data, and index information while managing text notes with creation and summarization capabilities.

Enables local MCP clients to list, read, write, and search notes in an Obsidian vault via an HTTP server running inside Obsidian.

Enables synchronization and management of FoundryVTT journal entries within a Notion Story Bible, including media export and conflict resolution tools. It provides a Model Context Protocol interface for LLMs to preview, apply, and monitor data mirroring between tabletop RPG platforms and digital databases.

Enables personal knowledge management through Claude Desktop, allowing users to capture thoughts, connect ideas, and reflect on thinking changes via natural conversation.

Enables AI agents to store and retrieve project context, bugs, decisions, and session logs by reading and appending markdown files in a local Obsidian vault, without requiring any cloud services.

A TypeScript-based MCP server that enables AI assistants to create, search, list, and delete notes with tags. Serves as a starter template for building custom MCP servers with tools and resources.

Local-first MCP server that extracts structured knowledge from markdown notes into SQLite with full-text search, enabling AI coding tools to retrieve relevant context offline at zero cost.

Allows AI models to interact with Obsidian notes through the Local REST API, enabling creation, reading, updating, searching of notes, and Git-based automatic backups.

Provides AI clients with per-project todo management and persistent context notes via MCP, enabling task tracking and resume summaries.

Enables AI agents and users to manage workspace files, monitor system metrics, take persistent notes, and retrieve weather data via MCP tools and resources.

Give your AI persistent, structured memory and let humans see it too. It stores project knowledge as Markdown files in a hierarchical tree, accessible via MCP tools and a built-in Web UI.

Automatically maintains a personal journal on behalf of an AI agent, storing text, photos, and voice notes as plain markdown files locally. Enables agents to add entries, recall memories, and reflect on patterns without user intervention.

An MCP server that turns any compatible AI agent into a Zettelkasten partner for creating atomic notes, forming semantic links, detecting clusters, and synthesizing insights from your existing knowledge.

Enables LLMs to access a user's personal writing context—voice, style, opinions, expertise, projects, and communication patterns—via curated markdown files, helping the LLM match the user's voice when generating written content.

Generates flashcards from Obsidian notes using obsidianki, with support for note patterns, card count, and deck selection.

Enables AI-powered journaling in Obsidian with dynamic reflection prompts generated from recent entries and automatic backlinks between related diary entries. Supports adaptive templates that learn from writing patterns and smart content similarity linking.

Enables AI assistants to read and write trading journal data, including trade logs, analytics, psychology audits, risk limits, playbooks, and daily reviews. Provides 38 tools across 7 domains plus workflow prompts for pre-market prep and post-session analysis.

Enables AI assistants to manage Apple Reminders on macOS using natural language, with support for creating, updating, and searching reminders including advanced features like recurring patterns, location-based triggers, and multiple alarms.

Integrates local Granola.ai meeting intelligence with Claude Desktop to enable searching and analyzing meeting transcripts, notes, and summaries. Users can perform natural language queries to retrieve meeting details, analyze participant patterns, and access full speaker-identified conversations.

A productivity coaching assistant designed for ADHD support that provides intelligent task management, goal tracking, and personalized recommendations. It utilizes a persistent memory system to learn user patterns and preferences, helping to reduce decision paralysis through actionable suggestions.