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Local MCP server for utility tools, shared notes, and searchable knowledge
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-starter" yet — see the docs or source repo.
Save the following project background as a persistent note and extract 5 keywords for future search: product goal, target users, core features, launch date, and risks.
A saved note record plus a list of tags or keywords for later retrieval.
Search the knowledge base for content related to “launch date” and “risks,” then summarize the key points from previously saved notes.
Matched notes or summaries that help quickly review previously stored information.
Write these meeting conclusions into shared notes and explain how other agents can continue searching and using the same data.
A persistently stored shared record and clear retrieval context that other agents can reuse.
Developers or product managers can use it to store unified notes and knowledge entries when multiple AI agents collaborate, avoiding fragmented context. Later agents can search and continue tasks on top of the same shared data.
In research or daily work, users can save information as persistent notes and retrieve it quickly by keyword. It fits scenarios where historical information needs to be revisited often.
In a local MCP environment, it can serve as a general utility server for AI clients. Besides knowledge storage and search, it also provides general utility tools.
It is a local MCP server that provides general utility tools, persistent note storage, and a keyword-searchable knowledge base. It also allows multiple agents to share the same data.
It is suited for scenarios where multiple AI agents need shared context, long-term note storage, or keyword-based knowledge retrieval. It emphasizes local deployment and shared data access.
The provided information only states that it is a local MCP server exposed via Streamable HTTP. For exact setup steps, runtime requirements, and dependencies, see the source repository.
Give AI agents semantic memory and web search for stronger retrieval and reasoning.
Provide persistent local semantic memory for MCP tools to store and search notes.
Give AI agents persistent knowledge-graph memory and cross-session retrieval.
Give AI agents persistent memory and semantic retrieval across conversations.
Let AI create, edit, search Markdown notes, and detect conflicts via MCP.
Secure offline MCP server for searching and editing local Markdown notes