VibeWise MCP
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- AlicenseCqualityCmaintenanceEnables coding agents to coordinate around shared project context, durable tasks, messages, artifacts, multimodal analysis requests, design-system checks, change proposals, and review requests through a compact MCP interface. It persists state locally in JSON and exposes tools, resources, and prompts for agent-to-agent workflows.9MIT
- AlicenseNot gradedqualityBmaintenanceEnables coding agents to make structured local decisions, choose bounded browser actions with approvals, and run coding workflows such as file relevance, context pruning, diff screening, and completion checks through MCP or CLI.Apache 2.0
- FlicenseAqualityBmaintenanceEnables coding agents to build and query a verified project memory through the MCP protocol, so skills, facts, and user preferences are captured as experience and adopted only after passing independent checks. It also briefs agents at each request, flags known pitfalls live, and runs an executable review before changes are finalized.4-
- AlicenseNot gradedqualityBmaintenanceConnects Claude Code, Codex, Cursor, and other coding agents to a project's idea, feature topology, and ordered task queue over MCP, so they can answer planning jobs, claim, build, and tick off implementation tasks live in your repository, or assemble a shareable PRD.1 npmMIT
- AlicenseNot gradedqualityBmaintenanceProvides AI coding agents with shared project memory, compact evidence-first context, status checks, durable handoffs, and encrypted cross-device checkpoints through MCP.1AGPL 3.0
TDQS
Scored across 10 tools
Most tools map to distinct lifecycle stages (start, checkpoint, confirm, report, reset), but vibe_status and vibe_read_notes both surface gate/state info, and vibe_pause's 'resume' overlaps with vibe_start's 'resumes a paused/pending state'. Descriptions mostly disambiguate these, so confusion risk is low.
Every tool uses the same vibe_ prefix with a consistent snake_case noun/verb (vibe_status, vibe_start, vibe_checkpoint, vibe_map_update). No camelCase or mixed conventions, so the pattern is fully predictable.
10 tools is well within the ideal 3-15 range and each corresponds to a distinct step of the learning-gate workflow. No tool feels redundant or filler.
The surface covers the full learning lifecycle: activation, onboarding, checkpoints, confirmation, reporting, map maintenance, pause/resume, reset, and state reading. Minor gap: no explicit way to answer/close an onboarding session or query individual notes, but agents can work around these.