Productivity · Remote MCP server
MemQ Team Sync
Shared project memory that keeps teammates and AI agents aligned across sessions.
What the MCP Registry states
The entry as published to the official MCP Registry (read 2026-10-04), latest version.
- Registry name
ai.multinex/memq- Version
- 2.8.1
- Status
- Active
- Category
- productivity
- Transport
- Streamable HTTP, stdio (local process)
- Package
- OCI image (Docker)
- Published
- 2026-09-07
- Updated
- 2026-09-07
- Publisher
- ai.multinex
- Repository
- github.com/multinex-ai/memq (folder mcp-server)
- Source
- Registry API entry
Remote endpoints
| Transport | URL | Headers declared |
|---|---|---|
| Streamable HTTP | https://mcp.multinex.ai/mcp/v1 | None |
Packages
| Registry | Package | Version | Transport |
|---|---|---|---|
| OCI image (Docker) | ghcr.io/multinex-ai/memq-mcp-server:registry-2.8.1 | — | stdio |
How to connect MemQ Team Sync
MemQ Team Sync is a remote MCP server: there is nothing to install. Its endpoint is https://mcp.multinex.ai/mcp/v1, served over Streamable HTTP. In an assistant that accepts remote MCP servers (often under a setting named connectors, integrations or tools), add a new server and give it this URL; in a client configured by file, add it as a remote (HTTP) server with the same URL.
No headers are declared in the registry entry. If the server needs you to sign in, a client that supports MCP authorization opens the service's own sign-in page when it first connects.
It can also run locally from a container image in an OCI registry such as Docker Hub or GitHub Container Registry: ghcr.io/multinex-ai/memq-mcp-server:registry-2.8.1. It speaks MCP over stdio, so the client starts it as a program and talks to it through standard input and output. It needs Docker or another OCI container runtime; started with docker run — the usual command is docker run -i --rm -e MEMQ_API_KEY ghcr.io/multinex-ai/memq-mcp-server:registry-2.8.1. It reads the environment variable MEMQ_API_KEY (required, secret); set it in the client's configuration for this server.
In the mcpServers JSON format that many desktop and editor MCP clients read, the entry looks like this (placeholders in angle brackets):
{
"mcpServers": {
"memq": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"MEMQ_API_KEY",
"ghcr.io/multinex-ai/memq-mcp-server:registry-2.8.1"
],
"env": {
"MEMQ_API_KEY": "<secret>"
}
}
}
}Derived from the registry entry, not tested here. What the server does, and on what terms, is set by its publisher; check its repository or website before giving it access to your accounts or files. How to add an MCP server to an assistant · Before you connect
More productivity servers
| Server | Runs |
|---|---|
| MemocoreShared memory for all your AI agents, your whole team and every MCP client — save, search, recall. | Remote · HTTP |
| MemolSearch, read, create and edit your Memol notes from Claude. Team note-taking with AI search. | Remote · HTTP |
| memoricProvenance-first database for teams and agents: every value carries sources, rules and coverage. | Remote · HTTP |
| MemorySync DocumentationSearch and read the current MemorySync documentation while writing integration code. | Remote · HTTP |
| MemsideAI continuity and Library Templates: memories, profiles, rules, tasks, and checkpoints. | Remote · HTTP |
| memsproutShared AI-context layer for teams — persistent memory your agents search and update over MCP. | Remote · HTTP |
| MengramLong-term memory for AI agents: semantic facts, episodic events, and procedural workflows. | Remote · HTTP |
| MensoAI users run real tasks on your live site and show where they get stuck, with a replay of every step. | Remote · HTTP |