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Provides local Git-history memory for coding agents with reverts and annotations.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.promptwheel-ai/logbook" MCP server from askskill: Run: claude mcp add 'io-github-promptwheel-ai-logbook' -- npx -y @promptwheel/logbook-mcp
Using the local Git history of the current repository, identify the most frequently changed files or areas and mark likely hotspot modules.
A list of hotspot files or code regions ranked by change frequency, with brief notes.
Check the repository’s local Git history for revert-related records and summarize which changes were previously rolled back.
A summary of revert-related records to help identify changes that need caution.
Extract annotations or suppression information related to the current file from local Git-history memory to explain past decisions.
Relevant historical annotations, suppressions, and any likely context for the current file.
When using an AI coding agent, developers can use local Git-history memory to review past reverts, suppressions, and annotations. This helps the agent understand which changes previously caused issues and which areas need extra care.
When maintaining a frequently changing repository, teams can use hotspot information to identify files or modules that are repeatedly modified. This makes it easier to prioritize reviews of higher-risk areas.
It is an MCP tool that provides local Git-history memory for coding agents. Known capabilities include working with reverts, suppressions, hotspots, and annotations.
From the description, it works with local Git history. For whether it also supports remote services or other data sources, see the source repository.
The provided material does not include installation steps or runtime prerequisites. For setup, runtime, and configuration details, see the source repository.
Give AI assistants long-term memory with logged facts, decisions, and history.
Give AI coding agents persistent project memory that detects stale knowledge.
Give coding agents project memory verified against files and Git.
Provide persistent, verified memory and guardrails for AI coding agents.
Give local agents correction-first memory and warning adherence tracking for safer outputs.
Give AI coding agents searchable local project memory with safe structured updates.