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Gives coding agents a persistent log for planning and tracking project work.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-waggle" yet — see the docs or source repo.
Please organize and log today's research findings for the tecture-graph project, including background, options compared, final judgment, and follow-up items to verify.
A structured project research log entry that can be reviewed later and used for handoffs.
Write this round of feature test results into the project log, including scope, passed and failed items, reproduction steps, and issues that need follow-up.
A clear test record that helps the team track quality status and unresolved issues.
Based on the past week's development activities, update the tecture-graph project progress with completed work, current blockers, and next steps.
A project progress update that supports ongoing oversight and planning.
Developers or coding agents working on the tecture-graph project can store research, development activity, and test results in one persistent place to reduce context loss. This also helps future contributors understand the project history quickly.
Project leads or team members can use it to continuously record progress changes, milestones, and unresolved issues for better oversight. It is suited to development work that needs long-term tracking.
It is an MCP server for planning and overseeing the tecture-graph project. It gives coding agents a persistent place to log research, development activities, test results, and project progress.
Based on the description, it can log research, development activities, test results, and project progress. These records are mainly used for planning, oversight, and ongoing tracking.
The provided material does not include installation or configuration steps. For integration details, runtime requirements, and dependencies, see the source repository.
Enable AI coding agents to communicate, share state, and coordinate work in real time.
Give AI coding agents filesystem, Git, database, and compute tools via MCP.
Monitor structured data changes and let agents pull real incremental updates.
Connect coding agents in a secure network for collaboration and gated execution.
Connect local AI coding agents to chat, delegate, and collaborate privately.
Pause AI workflows for a set time before resuming automated tasks.