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.
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.
MARM MCP provides persistent memory and structured session context beneath any AI tool, so your agents learn, remember, and collaborate across all your workflows.
Description: Persistent memory for AI agents with rollback, audit trails,
semantic search, and knowledge graph. Zero-config local SQLite or cloud API.
23 tools, 6 resources, 3 prompts.
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 local, scoped access to DEVONthink 3 on macOS, enabling search, reading cited text, following references, and previewing document organization from an AI assistant.
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.
Persistent AI memory server with 3-layer hybrid search (vector + FTS5 + keyword), confidence scoring via Reciprocal Rank Fusion, episodic/profile memory, and 16 tools. Zero LLM dependency. Works standalone with Claude Desktop and Claude Code. MIT licensed.
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 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.
DescriptiShared knowledge cache for AI agents — cache-first search saves tokens and avoids redundant web searches. Cross-agent deduplication with trust scoring. Human Bridge for blocked/paywalled content. MCP-native (FastMCP), ChromaDB-backed. 3 tools: agenthive_search, agenthive_contribute, agenthive_stats.
Enables AI agents to store, search, list, and forget long-term memories in a fixed namespace backed by S3-compatible storage, with optional read-only mode and provenance-tagged recall results.
Persistent AI memory server with 3-layer hybrid search (vector + FTS5 + keyword), confidence scoring via Reciprocal Rank Fusion, episodic/profile memory, and 16 tools. Zero LLM dependency. Works standalone with Claude Desktop and Claude Code. MIT licensed.
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.
Enables comprehensive chess analysis through Stockfish engine integration, positional evaluation, puzzle training, game review, and access to extensive chess databases. Provides visual board rendering, interactive game viewers, and tactical puzzle training with 3+ million problems from Lichess.
A living X-Matrix your AI agent can read, challenge and update — 3-to-5-year objectives, annual objectives, initiatives, KPIs, correlations, review cadences, and a completeness score. Exposes 11 tools and 9 guided prompts over MCP with local or hosted transports.
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.
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.
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.
Zero-tool-call codebase intelligence for Claude Code and MCP clients. Automatically injects the right code context, functions, callers, and call chains, before the LLM starts thinking. Replaces 4-6 grep/read round-trips with a single 5ms hook injection, cutting token usage by 3-8x.
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.
MCP Hub is a self-hosted AI operations platform that provides a unified MCP gateway with semantic tool routing, persistent vector memory, automation, and multi-agent flows. It enables connecting any MCP client to 130+ tools across 12 integrations through just 3 hub endpoints.
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.
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.
