Repowise

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Codebase intelligence layer: code health scores, auto-generated docs, git analytics, dead code detection, and architectural decisions for AI agents and humans.

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★ 6.2k Stars Category · Dev Tools Very popular Source revision c2f915d07c11

66FMRS · C

Reliability

9/20

Security and permissions

13/20

Maintenance

15/20

Documentation

16/20

Setup experience

13/20

Repowise is a feature-rich code intelligence MCP server with a comprehensive toolset and deep analysis capabilities. It's particularly strong for teams wanting to cut AI token usage, gain code health and change risk insights, and auto-generate documentation. However, be mindful of index time, privacy configuration options, and the reliance on LLM providers for optional prose. Overall, it provides clear value for medium-to-large codebases and AI-powered development workflows.

Read the FMRS scoring method →

Repowise is an open-source code intelligence engine that provides AI coding agents with deep repository insights via MCP: a single index builds dependency graphs, git history, generated wiki docs, architectural decision records, and defect-validated code health scores. It reduces token consumption by up to 97% for context loading, offers concrete refactoring plans, and supports multi-repo workspaces. Self-hosted and local-first, your code never leaves your machine unless you opt into LLM-generated prose.

Tools

get_overview

Provides architecture summary, module map, entry points, and git health. The first call on any unfamiliar codebase.

get_answer

Hybrid retrieval (full-text plus vector) with PageRank bias and 1-hop graph expansion returns a cited answer with a calibrated retrieval_quality score.

get_context

Generates triage cards for files, modules, or symbols: summary, signatures, hotspot bit, governing decisions, and symbol IDs. Batch multiple targets in one call.

get_symbol

Returns source for one indexed symbol with exact line bounds. Cheaper and safer than Read plus offset math.

search_codebase

Semantic search over the generated wiki, filterable by kind (implementation/test/config/doc), tagging each result's search method.

get_risk

Analyzes hotspots, dependents, co-change partners, ownership, test gaps, and bug history. Pass changed_files for PR mode to get a directive block.

get_change_risk

Scores pre-merge defect risk for a commit or range based on diff shape, ranked as a percentile against recent commits, plus tests coverage proves it touches.

get_why

Retrieves architectural decisions, evidence spans, and supersession lineage. Falls back to git archaeology when no decisions exist.

get_dead_code

Returns unreachable code by confidence tier with cleanup-impact estimates, and cross-repo consumer detection in workspace mode.

get_health

Returns per-file marker scores across three signals: defect risk, maintainability, and performance. Includes coverage, trends, self-check, and structured refactoring plans.

Setup

Install with pip install repowise, then index your repo: cd /path/to/your/repo && repowise init (prompts for mode; use --no-prose -y for keyless structural docs).
Start the MCP server: repowise mcp (run from the repo directory), then configure your client:

- Claude Code: claude mcp add repowise -- repowise mcp or plugin /plugin install repowise@repowise.
- Codex CLI: add [mcp_servers.repowise] command = "repowise" args = ["mcp"] to ~/.codex/config.toml.
- Other MCP clients: configure command repowise mcp with the repo path.

First real call: ask your agent to use get_overview or get_context.

Fit and risk

Best for

Developers who want deep codebase insights locally without uploading code to the cloud.
Teams needing pre-merge change-risk scoring and code health metrics.
Those looking to provide structured context to AI coding agents to save tokens and improve accuracy.

Not for

Users wanting a fully managed cloud service without any local installation (use hosted repowise.dev).
Slow index times may be a concern for large repos (e.g., 366s for Django), so not ideal if you need instant setup.
Non-technical users unfamiliar with command-line tools and MCP configuration.

Required permissions

Read access to the specified repository's file system (mandatory).
Optional: send code snippets to LLM providers for prose generation (if AI-generated docs enabled).
Optional: write access to Git or GitHub for PR bot comments if installed.
Read access to Git history for analysis.

Risks and side effects

Privacy: while local-first, enabling LLM prose or using hosted versions sends code to third parties.
Performance: indexing large repositories can take minutes, slowing initial setup.
Reliance on Python environment and pip; potential dependency conflicts.
Accuracy: heuristic-based scores and generated docs may be imperfect, especially for less-supported languages.

Troubleshooting

Index failures: ensure the repo path is correct, run `repowise init --path /absolute/path` and check error output.
MCP connection issues: confirm `repowise mcp` is running in the repo directory and client config matches.
Outdated index: run `repowise update` or `repowise watch` to keep in sync.
No search results: ensure wiki is generated (`repowise generate`) and embedder configured for semantic search (Ollama works keyless).
For security, run `repowise doctor` to check setup and API keys.

Use cases

Onboard to a new codebase: use get_overview to get architecture and entry points.

Pre-merge risk assessment: use get_risk and get_change_risk to identify high-risk files and impact.

Discover dead code and refactoring opportunities: use get_dead_code and get_health for concrete plans.

Generate documentation automatically: index and search the wiki via MCP.

Reduce AI agent token consumption: leverage precomputed context and command distillation (`repowise distill`).

Supported clients

Claude CodeFull support

Codex CLIFull support

CursorFull support

VS CodeFull support

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