AI Conversation Logger

You have an intelligent MCP server that automatically logs and organizes AI-assisted conversations, helping you preserve project history with structured summaries, tags, and searchable context for retrospection and auditing.

How to use

Connect your MCP client to the Conversation Logger MCP to start recording AI-user interactions. The server stores each conversation with the user request, the AI’s execution plan, a concise summary, any file operations performed, and relevant tags. You can then search across conversations by keywords, file names, tags, or date ranges, and receive context suggestions based on your current work.

Practical usage patterns include logging all feature discussions, bug fixes, code explanations, and configuration changes. Use tags to categorize by project or module, and include an optional title for easy scanning. Rely on the AI-driven content decisions to capture the most useful elements for future reference.

How to install

Prerequisites: ensure you have Node.js installed (LTS version recommended). You will also need a working MCP client that can connect to this server via the supported channel.

Install dependencies and build the project, then configure your MCP client to use the logger server.

npm install

npm run build

{
  "mcpServers": {
    "conversation-logger": {
      "command": "node",
      "args": ["/path/to/ai-conversation-logger-mcp/dist/index.js"]
    }
  }
}

Restart your MCP client after applying the new configuration to connect to the logger server.

Configuration and startup

The server is configured as a local, stdio-based MCP endpoint. You start it by invoking Node on the built index, as shown in the configuration snippet. Ensure the path to the built file matches your environment, then restart your MCP client to apply changes.

Key commands you will use in practice include running the build before starting the logger and then restarting your client to establish the connection.

Available tools

log_conversation

Records every AI-user interaction with structured information including user requests, AI plans, summaries, file operations, and optional metadata like title and tags.

search_conversations

Allows multi-parameter search across conversations by keywords, file patterns, date ranges, projects, and tags with optional result limits.

get_context_suggestions

Provides relevant historical context based on the current input, involved files, and project scope to assist AI retrospection.