Local-first codebase intelligence MCP server for AI coding agents.
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Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"mako-ai": {
"args": [
"mcp"
],
"command": "agentmako"
}
}
}Are you the author?
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agentmako is a local-first codebase intelligence engine for AI coding
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y 'agentmako' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked agentmako against OSV.dev.
Click any tool to inspect its schema.
mako-guideMako-specific guidance for which tools to call and how to interpret their results
mako-discoveryDiscovery guidance for exploring the codebase
mako-traceTracing guidance for following code flow
mako-neighborhoodsGuidance for exploring code neighborhoods and relationships
mako-graphGuidance for understanding code dependency graphs
mako-databaseGuidance for database inspection and review
mako-code-intelCode intelligence guidance
mako-workflowWorkflow orientation guidance
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One local source for the MCP servers, tools, and memory your AI coding agents share, synced into each tool's native config with a review gate and a receipt for every change. No daemon, no lock-in.
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agentmako is a local-first codebase intelligence engine for AI coding tools.
It gives agents like Codex, Claude Code, Cursor, and local harnesses a compact Reef-first tool surface for understanding a project before they edit it. Mako indexes your repo, builds local SQLite-backed facts, tracks diagnostics and review notes, and answers evidence-backed questions instead of making the agent orchestrate broad tool chains or rediscover everything with raw grep.
Mako is built for the first mile of coding-agent work:
What files matter? What routes, symbols, tables, diagnostics, and prior findings are relevant? What should the agent read next?
agentmako mcpagentmako dashboardreef_ask across code, database, findings,
diagnostics, instructions, freshness, and literal checksmako_helpcontext_packet_hints on tool results so agents get result-specific next stepsreef_status, reef_verify, reef_impact,
live_text_search, lint_files, and tool_batch.mako/rules YAML rule packs, including primitive
cross-file helper-bypass rules via canonicalHelperEverything important runs locally. No hosted service is required.
Requires Node.js 20 or newer.
npm install -g agentmako
Confirm the CLI is available:
agentmako --version
agentmako doctor
You should see green checks for configuration and the local API service.
Prefer to build from source (e.g. to contribute)? See Develop From Source at the bottom of this file.
Go to the project you want Mako to understand:
cd C:/path/to/your/project
Attach and index it:
agentmako connect . --no-db
Use --no-db for the first run. It gets the code intelligence path
working before adding database scope.
agentmako status .
agentmako tool list
Run a real Reef query:
agentmako --json tool call . reef_ask "{\"question\":\"where should I inspect auth route state?\"}"
If that returns an evidence-backed answer, facts, findings, or next queries, the core setup is working.
reef_ask plans over code, database, durable findings, diagnostics, and exact
literal evidence. App-flow questions favor file, route, and finding evidence;
RLS/schema questions favor database facts and review comments. To inspect
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