repowiseMCP tools — overview
The seven MCP tools repowise exposes to Claude Code and any MCP-compatible AI agent. Task-shaped, batched, and built to collapse long tool-call chains into one round-trip.
Most MCP servers expose tools that mirror data entities — one file, one symbol, one diff. That forces the agent into long sequential chains. Repowise exposes seven task-shaped tools instead. Pass multiple targets in one call. Get complete context — docs, ownership, metrics, history, decisions — back. Same answer, fewer round-trips, fewer tokens.
The seven tools
| Tool | What it answers | When the agent calls it |
|---|---|---|
get_overview | Architecture summary, module map, entry points, git health | First call on any unfamiliar codebase |
get_answer | One-call RAG: retrieves over the wiki, gates on confidence, synthesizes a cited answer | First call on any code question |
get_context | The workhorse — docs, symbols, ownership, freshness, optional source/callers/callees/metrics for any targets | Before reading or modifying code |
search_codebase | Semantic search over the full wiki | When get_answer returned low confidence |
get_risk | Hotspot scores, dependents, co-change partners, blast radius, reviewers, test gaps | Before modifying files |
get_why | Architectural decision search — NL query, path lookup, or health dashboard | Before architectural changes |
get_dead_code | Unreachable code sorted by confidence tier with cleanup impact | Cleanup tasks |
All seven are available in both single-repo and workspace modes.
In workspace mode, every tool accepts a repo parameter — pass a
specific repo name, or "all" to federate across the workspace.
Connecting
repowise init automatically writes .mcp.json at the project root
and (for Claude Code) registers the server in ~/.claude/settings.json.
You don't need to do anything else.
To wire up another editor manually:
{
"mcpServers": {
"repowise": {
"command": "repowise",
"args": ["mcp", "/path/to/your/project"]
}
}
}For the hosted version, use the HTTP transport with an API key from Settings → Editor.
Drop the same mcpServers block into ~/.cursor/mcp.json.
Add the same mcpServers block to claude_desktop_config.json
(macOS: ~/Library/Application Support/Claude/,
Windows: %APPDATA%\Claude\). Restart Claude Desktop after editing.
Use the MCP extension and point it at repowise mcp <project>.
Add to Windsurf settings → MCP servers using the same command.
A real five-call task
"Add rate limiting to all API endpoints." Without repowise, an agent greps and reads files for ~30 calls. With repowise, five:
get_overview()
get_context(targets=["middleware", "api/routes", "payments"])
get_risk(targets=["middleware/auth.ts"])
get_why(query="rate limiting")
search_codebase(query="rate limit OR throttle OR retry")That's the design point. Each call returns a task-shaped slice of the codebase — not a single file or symbol — so the agent reaches a decision in one or two passes instead of ten.
Why only seven
Repowise's internal modules expose more graph navigation primitives (callers/callees, dependency paths, graph metrics, execution flows, community detection). Those are powerful for building tools, but they push complexity onto the caller — every navigation step is another LLM round-trip.
The seven public tools are deliberately one level up. get_context
absorbs callers, callees, metrics, and community into a single call
via its include parameter. get_risk rolls up hotspot, dependents,
co-changes, reviewers, and test gaps. get_why covers natural-language
search, path lookup, and the global health dashboard.
If you're building on top of repowise programmatically, the
packages/server Python API exposes the lower-level primitives. The
MCP surface is what AI agents see — and AI agents do better with
fewer, richer tools.