repowise-dev/repowise
by Various
Codebase intelligence for AI and humans: code health scores, auto-generated docs, git analytics, dead code detection, and architectural decisions via MCP.
MCP
repowise-dev/repowise
Added 3 Sept 2026
Overview
Repowise analyzes codebases to produce code health scores, auto-generated docs, git analytics, dead code detection, and architectural decisions. It exposes these capabilities through MCP, a protocol that lets AI assistants and human developers query the same codebase intelligence.
Best for
Best for
Teams that self-host developer tooling and want a unified codebase intelligence layer for fluid between AI assistants and humans
Use cases
- Run code health scoring and dead code detection on existing repos
- Generate docs and git analytics from source and history
- Give AI coding assistants access to codebase insights via MCP
How to use
Install
pip install repowiseTools exposed
get_overviewget_context
Tested with
Claude Code, Codex, Cursor, VS Code
Notes
Repowise analyzes codebases to produce code health scores, auto-generated docs, git analytics, dead code detection, and architectural decisions. It exposes these capabilities through MCP, a protocol that lets AI assistants and human developers query the same codebase intelligence.
6,306 stars on GitHub. Last updated 2026-09-02. Licensed AGPL-3.0.
Use cases
- Run code health scoring and dead code detection on existing repos
- Generate docs and git analytics from source and history
- Give AI coding assistants access to codebase insights via MCP
Pros
- Combines multiple codebase diagnostics in one open source tool
- MCP integration lets AI agents use the same data as humans
- Active adoption with 6,300+ GitHub stars
Cons
- Requires self-hosting and setup for each codebase
- MCP design demands an MCP-aware client and environment
- No managed service, so maintenance and tuning are on the user
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Combines multiple codebase diagnostics in one open source tool
- MCP integration lets AI agents use the same data as humans
- Active adoption with 6,300+ GitHub stars
Cons
- Requires self-hosting and setup for each codebase
- MCP design demands an MCP-aware client and environment
- No managed service, so maintenance and tuning are on the user
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