"The Models Resource" matching MCP servers:

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.

Enables creating, managing, analyzing, and visualizing knowledge graphs with support for multiple graph types (topology, timelines, changelogs, requirements, knowledge bases, ontologies) including node/edge management and resource association.

Enables MCP-compatible agents to answer questions about you by exposing your CV through a tool, resource, and prompt.

A minimalist and elegant Model Context Protocol (MCP) server for fast context memo capture, keyword search, dynamic resource digests, and AI prompt workflows.

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.

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.

A custom Memory MCP Server that acts as a cache for Infrastructure-as-Code information, allowing users to store, summarize, and manage notes with a custom URI scheme and simple resource handling.

A note-taking knowledge base MCP server that enables CRUD operations on notes with tags, resource listing/detail, and prompt-based analysis and brainstorming.

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.

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.

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.

Hosted MCP gateway to the RTP global commons of neighborhood practice and relational tech knowledge. AI builders can query 275+ recipes, frameworks, and methodology docs for community organizing.

An MCP server that provides AI tools with relational tech principles, patterns, and the Studio library to guide builders in creating community-centered tools using embedded design.

Provides persistent memory for AI models by enabling the storage and retrieval of episodic, semantic, and procedural information through the memro protocol. It allows assistants to maintain long-term context via semantic search and chronological memory management.

Gives MCP clients a small remote Streamable HTTP tool surface for a Brain memory database, letting agents store facts, retrieve current-truth answers with evidence, delete memories, and check their effective identity. Each call is scoped by a customer identifier for per-customer isolation, and the endpoint can optionally be secured as an OAuth 2.1 resource server.