dekko
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@dekkogenerate a code map and index for the current repository"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
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), dekko's
structured queries used 3x–200x fewer tokens than the equivalent
Read/Grep workflow for the same task (repo orientation, outlining a
large file, tracing a symbol's callers/callees).
Task | Example repo (scale) | dekko | Read/Grep | Savings |
Repo orientation ( | awesome-go (10 files) | 308 tok | ~15,271 tok | ~50x |
Repo orientation ( | cline (2,730 files) | 1,202 tok | ~4,020 tok | ~3.3x |
Outline a large file | claude-code | 1,017 tok | 200,981 tok | ~197x |
Outline a large file | zed | 1,996 tok | 115,109 tok | ~58x |
Symbol lookup ( | tensorflow | ~811 tok | 61,656 tok | ~76x |
Symbol lookup ( | spring-boot | 759 tok | ~18,460 tok | ~24x |
Bundled context ( | zed | 2,984 tok | ~5,903+ tok (targeted) / ~164,571 tok (whole file) | ~2x / ~55x |
Bundled context ( | awesome-go | 617 tok | ~6,136 tok | ~10x |
dekko's cost stays roughly flat per query while Read/Grep scales
with file/repo size, so the ratio grows with scale. The win isn't
universal — small,
self-contained files and already-grep-friendly local symbols see
little to no benefit, and a few cases in the raw data are void because
the cheap answer was also an incomplete one. See
benchmarks/real-world-repos/
for the full per-task breakdown, methodology, and correctness caveats.
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.
Related MCP server: project-code-intelligence
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 restartExtras (dekko[all] for ~55 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.
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
/mapplugin, 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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