"Maintaining Consistent Behavior in System Prompts" matching MCP servers:

A local MCP server that transforms an Obsidian vault into a structured learning interface through tools for concept extraction and gap analysis. It enables AI agents to generate study plans and align note-taking with implementation projects for a more cohesive learning workflow.

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

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.

Empowers AI assistants with real-world capabilities including note management, Gmail integration, Google Calendar and Tasks management, and Git repository operations through the Model Context Protocol.

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 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.

Reads saved Keep My Prompts library and fetches prompts by name from any MCP client, read-only.

A personal knowledge base MCP server that allows AI assistants to manage notes, tasks, and ideas through tools, resources, and prompts.

A bookmark management MCP server that enables saving, searching, and managing bookmarks with tags and read/unread status, providing tools, resources, and prompts for integration.

Enables AI assistants to read, write, and manage Microsoft OneNote notebooks, sections, and pages through OAuth-authenticated MCP tools, resources, and prompts.

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.

Enables reading and updating relationship data (likes, dislikes, important info, feelings, closeness, notes) for characters in a multi-character system, supporting both private and public relations.

Enables AI agents to build and interact with mind maps on mindmap.io. Agents can create, read, update, and delete maps and nodes, run prompts on nodes, and auto-expand topics into follow-up questions.

Enables interaction with your Logseq personal knowledge management system via MCP, allowing retrieval of tagged notes and todo lists through Logseq's HTTP API.

Manages continuation notes for projects, enabling session handoff and historical browsing through a two-location storage system.

This MCP server enables AI assistants to perform calculations, fetch live weather, manage in-memory notes, access system info and note resources, and use reusable code review and summarization prompts.

Enables interaction with a file-based issue tracking and knowledge management system, allowing users to create, read, update, and move issues, as well as search and read notes within the UniHub Vault.

Enables AI assistants to read and write Memos notes through a Personal Access Token, with task-oriented search, precise editing, todo and tag management, attachment handling, exports, prompts, and resources. It supports both local stdio and multi-user Streamable HTTP transports, with safety switches such as read-only mode and restricted file access.

Enables everyday utilities like calculation, text analysis, encoding, hashing, UUID generation, datetime lookup, and note management through MCP tools, resources, and prompts.