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, 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.
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.
Metis gives AI agents expert know-how that named reviewers have approved, and only where its recorded conditions match the situation. High-risk situations go to a person. When an agent sees practice depart from procedure, it can send the expert one short question. Every retrieval is recorded on a hash-linked evidence chain.
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.
Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.
An MCP server that records decisions, rejected alternatives, and justifications, and retrieves them later to avoid re-litigating past choices. It stores data locally in SQLite and Markdown.
Memory vaults on your own machine, one per life domain, retrieved with a local BM25 index fused with vector search, where the human decides which vaults a model may answer from. Requires the Mnemosyne OS desktop app running locally; it also reports what other coding agents on the same machine are doing.
Digital identity layer for AI — your bio, career, skills, interests, and projects always available to every AI tool. Auto-generates profile from 342+ public APIs, 13 real-time plugins, YAML-based profiles with privacy-first local storage.
Framework-aware code intelligence MCP server that builds a cross-language dependency graph from source code. 53 integrations (Laravel, Django, Rails, Spring, NestJS, Next.js, and more) across 68 languages. 100+ tools for navigation, impact analysis, refactoring, security scanning, session memory, and CI/PR reports — up to 97% token reduction.
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.
Local-first memory engine for AI-agent teams: private/team/project ACL, associative recall, and federated sync across nodes. One SQLite file, no LLM required.
An MCP server that provides AI trading agents with persistent, outcome-weighted memory to learn from historical performance and detect behavioral biases. It enables agents to automatically adjust strategies and optimize position sizing based on context-aware recall of past trade outcomes.
Enables personal knowledge management through Claude Desktop, allowing users to capture thoughts, connect ideas, and reflect on thinking changes via natural conversation.
Historical geography for AI agents: 1,000+ polities with real GeoJSON borders, dynastic succession chains, events, cities and trade routes from 4500 BCE to today, each with cited academic sources and a confidence score. Wraps the free AtlasPI REST API (https://atlaspi.it) as 39 MCP tools — no API key required, Apache-2.0.
A codebase ontology and a cited wiki, both kept as Markdown in one folder on your disk. Typed graph answers — what a change affects, how two concepts connect, which wiki lines lack a citation — and every write lands as a Git diff a person reviews.
An MCP server for Claude Code that forces AI agents to document every change with rationale, rejected alternatives, and trade-offs, ensuring code maintainability and developer ownership during AI-assisted development.
