"The most widely used models" matching MCP servers:

Provides tools for managing quantitative research knowledge graphs, enabling structured representation of research projects, datasets, variables, hypotheses, statistical tests, models, and results.

Self-improving, verifiable memory for AI coding agents. Learns how you work, stops repeating mistakes, models each project, recalls the right lesson at the right moment. Every memory is signed and tamper-evident. Local-first.

The most complete Signal MCP server and CLI.

The most feature-complete MCP server for Obsidian vaults. 23 tools and 3 resources for search, read, write, tags, link analysis, graph traversal, and canvas support.

Provides access to the HUMMBL Base120 framework of 120 validated mental models organized across 6 transformations, enabling users to search, retrieve, and get AI-recommended mental models for problem-solving and decision-making.

Provides OpenCode with adaptive long-term memory by storing preferences, lessons, and usage history locally in SQLite, enabling AI to remember and adapt to users through context-based learning.

A local-first personal archive with a read-only MCP gateway that preserves files and AI conversations, derives evidence-linked memory candidates using local models, and enables bounded context retrieval for questions.

The simplest and most stable MCP server for Anki, enabling LLMs to perform core Anki operations (CRUD) directly without addons.

Enables AI agents to record and rank learnings, facts, and methods through a collaborative voting framework. It provides tools for agents to surface the most useful information across sessions using persistent memory storage.

A version-aware cache for web research that stores and serves documentation references, preventing models from re-researching the same topics across sessions.

This project implements a Model Context Protocol (MCP) server for connecting AI models with Obsidian knowledge bases. Through this server, AI models can directly access and manipulate Obsidian notes, including reading, creating, updating, and deleting notes, as well as managing folder structures.

Enables AI assistants to read, search, create, and organize notes in an Obsidian vault, with features like summarization and daily most valuable notes identification.

Provides access to Wikidata for Large Language Models through the Model Context Protocol, offering tools for entity search, detailed retrieval, SPARQL queries, relation exploration, and property-based searches.

Enables command-line interactive chat with Anthropic AI models, supporting document retrieval via @ references and slash commands for custom tool execution through the MCP architecture.

Enables AI models to interact with the Get笔记 (GetNotes) platform via its Open API. It supports managing notes, organizing knowledge bases, and handling tags through natural language commands.

A flexible memory system for AI applications that supports multiple LLM providers and can be used either as an MCP server or as a direct library integration, enabling autonomous memory management without explicit commands.

An MCP server that enables AI models to retrieve information from Ragie's knowledge base through a simple 'retrieve' tool.

Temporal knowledge graph for codebases that captures decision traces, links test failures to code changes, learns co-edit patterns, predicts regression risk, and enforces learned constraints at the edit boundary via a PreToolUse hook.

Enables searching and retrieving content from BookStack knowledge bases via the BookStack API. It provides structured page data with clean HTML-to-text conversion for seamless integration with AI models.

Turns arXiv and LaTeX mathematical papers into theorem dependency graphs, exposing MCP tools to load papers, list theorems, get theorem details, find dependencies, and locate where theorems are used.