A Model Context Protocol (MCP) implementation for the [MemOS](https://github.com/MemTensor/MemOS) API service. This package provides a command-line
Most knowledge and memory work still happens through a UI a human drives. Memos Api MCP MCP server moves it into the conversation instead. A Model Context Protocol (MCP) implementation for the MemOS API service. This package provides a command-line interface to interact with MemOS API using MCP standards.
The server publishes 14 tools. What each one is for:
Parameters — - conversation_id: Unique identifier of the conversation associated with the feedbackmessages — Array of messages containing role and content informationrole — Role of the message sender (user or assistant)content — Message contentchat_time — (Optional) Message timestampfilter — (Optional) Filter conditions, used to precisely limit the memory scope before retrievalknowledgebase_ids — (Optional) Array specifying the knowledge bases to searchinclude_preference — (Optional) Enable preference memory recall. Default: truepreference_limit_number — (Optional) Max preference memories to return. Default: 9, max 25include_tool_memory — (Optional) Enable tool memory recall. Default: falsetool_memory_limit_number — (Optional) Max tool memories to return. Default: 6, max 25include_skill — (Optional) Enable Skill recall. Default: falseConfiguration is passed through the environment: MEMOS_API_KEY, MEMOS_USER_ID, MEMOS_CHANNEL. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
The server ships on npm as @memtensor/memos-api-mcp, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
This sits in the knowledge and memory group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Memos Api MCP's toolset — Parameters, messages, role and 11 more — is a fair guide to whether it matches your workflow. It is maintained by memtensor; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| Parameters | - conversation_id: Unique identifier of the conversation associated with the feedback. |
| messages | Array of messages containing role and content information. |
| role | Role of the message sender (user or assistant). |
| content | Message content. |
| chat_time | (Optional) Message timestamp. |
| filter | (Optional) Filter conditions, used to precisely limit the memory scope before retrieval. |
| knowledgebase_ids | (Optional) Array specifying the knowledge bases to search. |
| include_preference | (Optional) Enable preference memory recall. Default: true. |
| preference_limit_number | (Optional) Max preference memories to return. Default: 9, max 25. |
| include_tool_memory | (Optional) Enable tool memory recall. Default: false. |
| tool_memory_limit_number | (Optional) Max tool memories to return. Default: 6, max 25. |
| include_skill | (Optional) Enable Skill recall. Default: false. |
| skill_limit_number | (Optional) Max Skills to return. Default: 6, max 25. |
| relativity | (Optional) Relevance threshold (0-1) for recalled memories. A value of 0 disables relevance filtering. |
{
"mcpServers": {
"memos-api-mcp": {
"command": "npx",
"args": ["-y", "@memtensor/memos-api-mcp"],
"env": {
"MEMOS_API_KEY": "your-api-key",
"MEMOS_USER_ID": "your-user-id",
"MEMOS_CHANNEL": "the-site-where-you-are-seeing-this-document"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| MEMOS_API_KEY | Credential the server authenticates with. | Yes |
| MEMOS_USER_ID | Configuration value read at startup. | Optional |
| MEMOS_CHANNEL | Configuration value read at startup. | Optional |
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.