Enables persistent, auditable business operations state by storing knowledge, evidence, decisions, tasks, execution receipts, and review results in a local SQLite database, so different sessions can continue the same project.
Read-only MCP access to the p-e relay store: an append-only corpus of provenance-bearing coordination records, with tools to fetch a record by id, list records and replies, and check existence. This image serves a snapshot frozen at build time; append_relay is listed but refused, and deposits to the live store require an HMAC-signed request.
Provides spaced-repetition scheduling with the SM-2 algorithm as MCP tools, enabling agents to manage review items like flashcards or vocab locally. It supports adding, grading, and listing due items, plus optional pro features for export and forecast.
Self-learning memory for AI coding agents. Observes tool sequences, user preferences, and recurring fixes — auto-promotes high-confidence patterns into behavioral rules. 22 tools, 2 prompts, SQLite-backed, zero config.
Enables persistent, project-scoped memory for OpenAI Codex Desktop and CLI on Windows, capturing prompts and responses through managed hooks, with federated recall across projects and optional local graph extraction.
An MCP server for managing Wiki.js 2.x pages through its GraphQL API, enabling search, read, create, update, move, delete, version history, and asset listing.
Enables Claude Code to control a Planka 2.x board via 15 MCP tools, with writes re-read and verified so reported success matches the board, plus an optional workflow that turns the board into durable agent memory.
A multimodal local memory MCP server that lets AI agents search across text, PDFs, images, audio, and video using Gemini Embedding 2 and a local ChromaDB.
Provides a compiled knowledge substrate, extracting atomic claims from an append-only capture log and querying a bitemporal claim graph over MCP. Read-only by default, with opt-in writes for trusted sources.
Enables Claude and ChatGPT to share a single study-progress database for interview prep, coursework, and spaced repetition, with per-user data isolation and server-side SM-2 scheduling for due reviews and study plans.
Integrates with Get笔记 (GetBiji) API to enable AI-powered knowledge base search and retrieval. Provides tools for enhanced semantic search, raw content recall, and rate limit monitoring.
Enables AI coding agents to create flashcard decks, list cards due today, and record 0–5 review scores so an SM-2 spaced-repetition algorithm schedules when each card appears again. All data is kept in a local SQLite file, so it works offline with no accounts, network calls, or cost.
Enables AI agents to safely write to and read from persistent memory by validating every write through three independent consensus nodes, storing memories only on 2/3 agreement and quarantining poisoning attempts.
MCP server for building and exploring OWL 2 ontologies using AI agents, with tools for axiom management, structural pattern matching, and persistent selections.
Local-first markdown vault with a built-in MCP server (streamable HTTP). 16 tools and 2 resources for Claude Code / Desktop / Cursor: read/write/search plus context_for_query, find_orphans, weekly_digest, compare_notes, semantic_outline. Per-folder agent permissions, LanceDB vectors, local Xenova ONNX embedder swappable to Ollama. Single Bun binary. AGPL.
Enables AI hosts to preserve and resume task and project state across conversations, process restarts, and worker or model replacement by storing structured records, snapshots, receipts, and bounded context views through a local stdio interface.
Enables AI models to store lived queries, source text, embeddings, fractal paths, capsules, and chained digests, then recall them through REST and MCP with a signed append-only continuity record.
Fully local, privacy-first MCP server that turns exported X/Twitter bookmarks into a searchable, enriched knowledge base for Claude Desktop and Claude Code.
A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.
Resume-as-an-Agent (RAAA) or Portfolio-as-an-Agent (PAAA) allows Agents to talk to your resume, find out if you are available for work, and more. Use the MCP to allow recruiters, managers, and anyone with an agent to "talk" to your resume.
Enables users to create, review, and manage flashcards using the SM-2 spaced repetition algorithm for optimized learning. It supports organizing cards into projects and automatically handles review scheduling based on user performance.
Provides LLMs with persistent memory across conversations using a knowledge graph that stores entities, relationships, and observations with support for PostgreSQL or SQLite backends.
Provides a unified multi-agent coordination layer with shared memory, wiki, skills, and file management via MCP tools, enabling agents to access and collaborate on resources in a single Python process.
Enables AI agents to interact with the AI Network blockchain by managing accounts, submitting transactions, and reading database values. It supports registering Hyper Agents and accessing a Layer 2 DAG-based shared agent memory system based on staking status.
A knowledge graph MCP server that indexes your codebase into a local SQLite graph and provides tools for project mapping, file summaries, and impact analysis, drastically reducing token usage in AI coding assistants.
A lightweight MCP server that adds spaced-repetition flashcards (SM-2) to AI assistants, storing data locally in SQLite and enabling card creation, review, and scheduling.
Gives Claude persistent memory across sessions by recalling stored embers through graph-aware retrieval that fuses semantic embeddings, BM25 lexical search, engagement signals and PageRank-style traversal, with superseded memories hidden unless historical recall is requested. It also archives full session transcripts so past conversations and compacted context remain searchable.
Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.
Enables an AI assistant to create, search, and manage locally stored spaced-repetition flashcards, quiz users in chat with front-only prompts, and grade their answers using SM-2 scheduling.
Je dois répondre en JSON avec name et description. Name = "jevmem" (le nom du serveur MCP publié). Description : décrit ce que le serveur permet, sans répéter le nom. Par exemple : "Saves decisions, rules, bugs, to-dos and dead ends from Claude Code and Cursor chats into a versioned JEVMEM.md file in the repository, then brings the relevant lines back in later sessions and checks tool calls against saved rules before they run." Je dois vérifier : ne pas citer de personne/mainteneur/organisation. Ne pas répéter le nom. Deux phrases max. Éviter de mentionner "jevmem" comme nom. Je peux dire "Saves..." sans nom. Ça mentionne JEVMEM.md, ce n'est pas le nom du serveur mais le fichier, c'est autorisé ? La consigne : la description ne doit pas reprendre le nom. Le nom est "jevmem" ; "JEVMEM.md" contient jevmem. Mieux éviter. Je vais dire "a markdown memory file in the repository". Éviter aussi les noms d'organisations (TypeSafe, Anthropic, Claude Code...). Claude Code est un produit, pas une personne/organisation. Mais prudence : ne pas citer de mainteneur ou d'organisation. Je peux dire "coding-agent chats". La description doit dire ce que le serveur permet. Je vais faire deux phrases.
An MCP server that provides version-pinned, deterministic documentation sourced from DevDocs.io to AI assistants (Claude, RooCode, Cline, Copilot etc.) and also via offline mode.
Not via Scraping! But using the supported downloading option from devdocs.
Enables AI agents to use a neuro-symbolic memory fabric with bi-temporal knowledge graph and holographic VSA, providing tools for adding, searching, temporal queries, auditing, and proving memories with cryptographic provenance and zero-LLM ingest.
