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Dekko

by aahlijiaversion 1.9.0i★ –iRuns on your computer
Registry nameio.github.aahlijia/dekko

Install

Overview

Description from the maker (original English)

Static code map generator: MAP.md + map.json for any repo, plus a Claude Code /map plugin

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Developer tools

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "dekko": {
      "command": "uvx",
      "args": [
        "dekko",
        "serve",
        "--mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add dekko -- uvx dekko serve --mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "dekko": {
      "command": "uvx",
      "args": [
        "dekko",
        "serve",
        "--mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "dekko": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "dekko",
        "serve",
        "--mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account aahlijia, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Developer toolsi

  • More stars than 0% of the other 4919 servers in this category
  • Published to the registry more recently than 58% of the other 4919 servers in this category
  • Basic checks equal to or better than 100% of the other 4919 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Dekko safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.9.0) reached the registry 29 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.

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Desktop Commander

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Cloudflare MCP

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Ever watched your coding agent grep blind through a repo, open three files it didn't need, and burn fifteen thousand tokens just to answer "who calls this function"? That's the problem dekko exists to fix.

This demo runs on dekko's own repo (commit 835733c ) — clone it and run the same three commands yourself.

dekko is a fast, offline, dependency-free static code map generator and codebase indexer for LLM coding agents. It scans a repository with tree-sitter (no model tokens spent parsing) and writes:

  • `MAP.md` — a human-readable map: a per-directory overview, an embedded architecture diagram, load-bearing/orchestrator rankings, then every file's functions/methods with signatures, doc lines, and who calls and is called by whom.
  • `map.json` — the same graph in machine-readable form.

On top of the map, dekko gives an agent a token-cheap way to answer questions like "what does this file contain," "who calls this function," and "what do I need to safely change this" — without reading whole files. It ships as a CLI, a Claude Code `/map` plugin + MCP server (Model Context Protocol), and works with Cline too.

The result: 10x-300x fewer tokens than a plain Read/Grep workflow on the everyday tasks (orientation, outlining a large file, a symbol's callers), measured across 7 real, unmodified open-source repos on dekko 0.43.77. The floor, a small grep-friendly local symbol, is about 2.5x. The full breakdown is right below.

Why dekko?

Most agent workflows gather context by reading whole files or grepping across a repo — expensive, and it throws away structure (who calls what, what a function's fan-in/fan-out looks like). dekko instead parses the repo once into a call graph and answers targeted questions against it. Measured across 7 real, unmodified open-source repos (Go, TypeScript, Java, Rust, Python/C++ — up to 14k files, 172k symbols) on dekko 0.43.77, dekko's structured queries used 10x–300x fewer tokens than the equivalent Read/Grep workflow for the same task (repo orientation, outlining a large file, tracing a symbol's callers).

TaskExample repo (scale)dekkoRead/GrepSavings
Repo orientation (summary)awesome-go (10 files)359 tok~16,328 tok~45x
Repo orientation (summary)claude-buddy (57 files)~349 tok~113,514 tok (every source file)~325x
Outline a large fileclaude-code REPL.tsx (5,005 lines)~1,154 tok~223,963 tok~194x
Outline a large filecline SdkController.ts (84 KB)1,803 tok~21,023 tok~11.7x
Outline a directoryzed crates/git_ui/src (32 files)~35,778 tok~418,520 tok~11.7x
Callers of a symbolclaude-code errorMessage~602 tok~18,521 tok (323+ grep hits)~31x
Callers of a symbolzed MultiWorkspace.new (22 sites, 8 files)~809 tok~381,446 tok (read the caller files)~471x
External-API usageclaude-code chalk~798 tok~8,099 tok~10x
Bundled context (workset)awesome-go ToHTML263 tok4,104 tok~16x

dekko's cost stays roughly flat per query while Read/Grep scales with file/repo size, so the ratio grows with scale. It's fast in wall-clock terms too: mapping dekko's own ~4,300-symbol codebase from a cold cache takes about 5 seconds on all cores, an incremental remap after an edit is well under a second, and queries against the resulting map return instantly. The win isn't universal — small, self-contained files and already-grep-friendly local symbols see little benefit (the floor measured was about 2.5x). See benchmarks/real-world-repos/ for the full per-task breakdown, methodology, the original 2026-08 study, and the correctness caveats it raised and how they were closed.

Compared to tag-index tools like ctags/gtags, dekko resolves actual call edges (not just definitions), ranks files by load-bearing-ness, and speaks directly to agents over MCP or the CLI — no editor plugin required.

Install

uv tool install dekko      # or: pip install dekko / pipx install dekko

dekko --claude-install # add the /map command + MCP server to Claude Code, then restart

Extras (dekko[all] for 43 more languages, dekko[search] for embedding search), installing from a local clone, and uninstalling are in docs/install.md.

Quick start

cd my-project

dekko map # writes .dekko/MAP.md + .dekko/map.json dekko summary # ~40-line digest: dirs, hotspots, entry points

.dekko/ is git-ignored by default; the map regenerates on demand, so you rarely need to run dekko map again by hand. If your repo has languages outside the default Tier-1 set (Python, C, C++, JS/TS, Go, Java, Kotlin, Rust), dekko map will say so per file; install dekko[all] for 43 more languages (see Install) and re-run. The list says how well each one is read.

Documentation

  • docs/install.md — installation, extras, local clone, uninstall
  • docs/cli.md — every CLI command, symbol targets, excluding files, notes, daemon mode, language support
  • docs/claude-code.md — the /map plugin, push hooks, Claude Code skills, the MCP server, and Cline

Learn more

  • CHANGELOG.md — per-version history
  • CONTRIBUTING.md — dev setup, testing, releasing
  • benchmarks/ — token-efficiency measurements, including a 7-repo real-world comparison against a plain Read/Grep workflow
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