"Île-de-France Mobilités" matching MCP servers:

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.

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.

Durable, local-first memory for AI coding agents over MCP — zero-dependency (pure Python + SQLite/FTS5), curated and semantically de-duped. Works with Claude Code, Codex and any MCP host, and you own the data as plain rows.

French marketplace MCP server for artisanal hemp/CBD products. Exposes 10 tools (product search, personalized recommendations, comparison, producer info with geolocation, stock availability, market prices "Bloomberg CBD", complete CBD guides, news, wiki search, full wiki article) + 4 resources (catalog, producers map, CBD reference covering France + 12 EU countries, wiki catalog).

Provides tools for storing, searching, and managing memories with knowledge-graph enriched context via Hydra DB's agentic memory.

Connects AI agents to a collective creativity platform where they can publish, read, and explore creative connections (bisociations) between concepts from different domains.

Provides personalized Data Engineering learning updates by fetching recent news about DE concepts, patterns, and technologies, while tracking user knowledge to present only new information relevant to their learning journey.

Converts meetings, chats, and notes into reusable knowledge for a venture capital firm, enabling knowledge capture and retrieval via Claude or Codex.

Enables persistent, full-text-searchable context for AI agents, including session tracking, event logging, todo management, and tagged context retrieval.

Turns a folder of Markdown files into a searchable work journal that any LLM can read and write.

Provides QE Framework expert-library guidance with compact search and recommendation tools, returning full expert content only on explicit request.

Local MCP server exposing the dek-memory hybrid memory engine over stdio, with optional ChatGPT tools for managing account memories via a browser profile.

Provides a local, persistent context database for AI tools via MCP, enabling them to recall user preferences, decisions, and experiences across sessions. It is fully local with optional sync, and works with any MCP client.

Turns your Readwise library into a blazing-fast semantic search engine with a streaming MCP server for LLM clients, supporting vector search, nightly syncs, and deployment on serverless platforms like Vercel.

Provides tools for semantic search, CRUD, verification, and reindexing of documents in a local vector-based knowledge base, usable via HTTP or stdio.

Enables Claude to read, write, and search markdown notes stored in a private git repo via an MCP server integrated with Silverbullet editor.

A temporal knowledge graph system that enables users to record and query architectural decisions, implementation patterns, and project failures. It integrates with Claude to provide hybrid search, timeline tracking, and automated knowledge gap detection using graph analysis.

An AI-assisted, long-term memory system for IDEs, powered by Kuzu graph database. GraphMemory-IDE is an MCP server that provides semantic vector search, graph-based knowledge storage, and real-time analytics.

An embedded knowledge graph in one SQLite file, exposing a MCP server for agents to read and write entities and relationships with full audit trail and time travel.

MCP server providing Claude with persistent, local memory for tracking architectural decisions, dead ends, and project context across conversations.

A lightweight vector-based memory system for AI agents with tools to store, semantically search, and delete memories, using your own embedding model.

Enables Claude to write in your personal style by learning from your local documents. It provides statistical style context for natural language rewriting, all without any data leaving your machine.

Persistent semantic memory for AI agents using PostgreSQL and vector embeddings, enabling cross-session continuity and semantic search.

Enables semantic search and management of private, encrypted knowledge bases via MCP, supporting document upload, text and URL ingestion, document listing, and removal through natural language.

A methodology-driven MCP server that guides enterprise AI rollouts through a 5-phase pipeline with layered memory for experience reuse, enabling agents to manage engagements and crystallize skills.

A set of four local MCP servers that help solo developers maintain context across sessions by providing searchable memory, session journals, file checkpoints, and project overviews. All servers run locally with minimal dependencies and work with any MCP-capable client.

Enables agents to view active beliefs, read owner-selected source text, submit inferred extraction for selected sources, and consult bounded choices from a local personal profile.

Stores and retrieves project context using OpenAI vector stores, enabling AI editors to remember and recall project knowledge across sessions.

A local-first, LLM-agnostic MCP server that lets you ask hard questions about your documents, media, and code, and get traceable answers entirely offline.

A vector-DB MCP server that gives Claude conversational memory by storing solved problems and solutions as vectors and retrieving relevant ones when a new request resembles a past solution.