"Building Agentic AI Solutions" matching MCP servers:

Model Context Protocol (MCP) server for Joplin note-taking app, enabling AI to read, search, and modify notes via sandboxed scripts.

Long-term memory for Claude Desktop and Claude Code over plain markdown files on your machine. It returns nothing when the corpus has no answer, naming the terms that appear nowhere — no least-bad match dressed as an answer. And it verifies cited commit SHAs against git, so an agent can tell what actually shipped from what was only discussed and abandoned. Local; nothing is transmitted.

SeaMeet MCP connects Claude, Cursor, Codex, and other AI agents to SeaMeet meeting recordings, transcripts, AI summaries, screenshots, action items, webhooks, and desktop recording controls. Use it to search meeting memory, read synced cloud recordings, and automate meeting notes through the Model Context Protocol.

Enables Claude to control the AI Coach desktop pet, making the mascot react to the conversation, accept care, show messages, act out activities, preview local files, and manage user memos.

Enables operating Notion pages and databases through a single Markdown document, supporting read, write, search, list, update, schema, comment, delete, and move operations in one call.

Safe MCP server bootstrap for Outline Wiki providing connection validation, capability reports, and diagnostics in a read-only, agentic mode.

Exposes a Notion-synced AI OS as read-only MCP tools, enabling MCP-compatible AI clients to search the vault, fetch page contents, list projects, and check project statuses without scraping public links.

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

Enables Google Drive, Sheets, and Docs operations; ATS job-board reads and liveness checks; local notes and HTTP connectors through MCP.

Enables persistent memory for Claude Code sessions by recording observations and building knowledge graphs for cross-session learning and domain knowledge management.

Provides AI coding agents with a local, Markdown-first shared memory, enabling them to record decisions, failures, progress, and handoffs and retrieve them across sessions via MCP tools.

Enables AI agents to use Obsidian vaults as persistent, bidirectional knowledge workspaces with wikilink/backlink resolution, structured frontmatter/tag indexing, task aggregation, and Obsidian Headless Sync.

Provides a shared, persistent memory layer for AI assistants, letting them read and write to a local Obsidian vault through MCP with validation, secret rejection, and deduplication.

Enables interaction with Google NotebookLM through natural language to create and manage notebooks, add sources from URLs/YouTube/Google Drive, perform AI-powered research and analysis, generate audio podcasts, videos, infographics, and slide decks from notebook content.

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.

Unofficial Notion MCP server built on Notion's private API (token_v2 cookie). Gives LLM agents full read/write access to the entire workspace — no integration token and no per-page sharing.

A bridge server that allows LLM tools to interact with an Obsidian vault through a local REST API, enabling file operations, note management, and metadata access through natural language.

Enables AI coding agents to share a persistent, git-backed team memory, automatically committing and syncing decisions, solutions, gotchas, statuses, and handoffs across projects and MCP clients.

A Model Context Protocol server that connects Claude Desktop to AI Note task management, enabling task creation, updates and management through natural language 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.