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Expose a .mind folder for agents to read, search, and suggest changes.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mind-mcp" yet — see the docs or source repo.
Search the .mind/ directory for content related to “user interviews” and list the most relevant files with matching snippets.
A list of relevant files and snippets or a brief summary of the matches.
List files and subdirectories under .mind/ and show the structure hierarchically.
An overview of the .mind/ contents and directory structure.
After reading project notes in .mind/, propose a set of improvements, including which files should change and what should be updated.
Suggested changes based on existing file contents, without inventing unrelated functionality.
Developers or product managers can expose the .mind/ directory to an MCP agent so it can read existing notes, records, or context files. This helps the agent ground its responses and suggestions in existing information.
When researchers or team members need to find a topic, they can have the agent search within .mind/. It is useful for locating clues and relevant passages from stored memory files.
Teams can ask the agent to read the contents of .mind/ first and then propose file changes. This helps organize improvements without losing the existing context.
It is an MCP tool that exposes a .mind/ folder to MCP agents. According to the description, agents can read files, search memory, propose changes, and list directory contents.
The provided information shows it can read files, search memory content, propose changes, and list directory contents. For anything beyond that, see the source repository.
The available material does not provide installation steps, runtime details, or key requirements. See the source repository for prerequisites.
Give AI agents persistent memory and personal knowledge graph capabilities.
Manage persistent agent memories across global or repository-specific scopes.
Give AI agents persistent memory, recall, and context management across sessions
Connect local memory vaults to MCP for persistent AI context read and write.
Safely read, write, and search a local Markdown memory store via MCP.
Give local AI coding assistants persistent memory and semantic code search.