Stores and retrieves agent conversation memory through Membase's decentralized Unibase DA network.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.
One-click editor setup isn’t available for this listing yet — we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.
Inspect callable tools, capabilities, and parameters exposed to AI agents by Membase MCP.
The unibaseio/membase-mcp MCP server gives an MCP-compatible agent access to Membase memory. It is intended for storing conversation history, interaction records, and other agent memories in the Unibase DA network. The stored data can be visited through the Membase testnet hub at https://testnet.hub.membase.io/.
Memory is organized around conversations. The server keeps an active conversation context, lets the agent identify that context, and supports switching to another conversation. Messages can then be saved to the selected conversation or retrieved from it.
The server is launched as a local Python entry point through uv. At startup, configuration identifies the account used for uploads, the conversation whose history should be preloaded, and the instance identity. The documented variables are MEMBASE_ACCOUNT, MEMBASE_CONVERSATION_ID, and MEMBASE_ID.
The unibaseio/membase-mcp MCP server exposes these operations through MCP tool calls rather than requiring the agent to implement direct Membase protocol requests. A client can first obtain the current conversation ID, switch conversations when needed, save a message or memory, and request the last number of messages from the current conversation.
Clone the repository, enter its directory, and start the server with:
Set MEMBASE_ACCOUNT to the account used for uploading data. Set MEMBASE_CONVERSATION_ID to a unique conversation ID; the README states that this conversation's history is preloaded. Set MEMBASE_ID to the instance or sub-account identifier. The client configuration uses the uv command with the repository directory, followed by run src/membase_mcp/server.py.
The README lists Claude Desktop, Windsurf, Cursor, and Cline configuration examples. The directory path and environment values in that configuration must be replaced with values for the local installation.
The unibaseio/membase-mcp MCP server provides four documented functions:
get_conversation_id returns the current conversation ID.switch_conversation changes the active conversation.save_message stores a message or memory in the active conversation.get_messages retrieves the last requested number of messages from the active conversation.Together, these functions support loading an existing conversation, changing the memory context, recording new information, and reading recent history.
The provided material documents the server against the Membase testnet hub and does not describe production deployment, retention controls, access policies, message schemas, or synchronization behavior. It also does not specify an API-key authentication flow or provide a package-manager distribution command. Configuration therefore depends on running the repository's source entry point and supplying the three documented environment variables.
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