arbor
This server provides graph-based codebase analysis through two core operations:
Trace Logic Paths (
get_logic_path) - Follow the call graph to discover all dependencies and usages of a specific function or class, revealing how code flows through your projectAnalyze Refactoring Impact (
analyze_impact) - Calculate the "blast radius" of changing a code element to understand what will be affected before making modifications, including direct callers and transitive dependencies
Key capabilities:
Deterministic Results - Uses Arbor's semantic dependency graph for execution-aware analysis rather than text matching, with confidence scoring (High/Medium/Low)
AI Integration - Implements Model Context Protocol (MCP) enabling LLMs like Claude to query the graph directly for structurally-accurate code analysis
Multi-Language Support - Works across 10+ languages (Rust, TypeScript, Python, Go, Java, C/C++, C#, Dart, JavaScript) with cross-file symbol resolution
Local Privacy - All analysis happens locally with no data leaving your machine
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., "@arborshow me all functions that call the authentication service"
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.
v2.6.0 — Ground Truth · The graph was wrong in ways v2.5.0 made fast. Colliding symbols were silently dropped, resolution depended on hash order, every edge claimed certainty, and every exported TypeScript symbol was indexed twice. All fixed, all tested. Edge recall +14% on TypeScript, +44% on Rust; 25% of a TS graph was phantom nodes. Reproduce it yourself:
cargo run -p arbor-watcher --example graph_stats -- <dir>
Why Arbor
Most AI coding tools treat code as text. Arbor builds a semantic dependency graph — functions, classes, and modules as nodes; calls, imports, and inheritance as edges — then answers execution-aware questions with deterministic precision:
Question | Arbor answer |
If I change this symbol, what breaks? | Blast radius with depth, confidence, and risk level |
Who calls this — directly and transitively? | Caller/callee traversal on the call graph |
What's the shortest path between A and B? | A* path through real dependencies |
Is this PR too risky to merge? | CI gate on blast-radius thresholds |
No keyword guessing. No embedding hallucinations. One graph, every interface.
Where the graph is unsure, it says so — edges carry a confidence, and ambiguous resolutions are labelled rather than hidden. An honest unknown beats a confident wrong answer.
Related MCP server: CodeGraph CLI MCP Server
What's new in v2.6.0
Correctness, not speed. Each of these was silently wrong before.
Fix | Why it mattered |
Colliding symbols are kept |
|
Resolution is deterministic | Same-directory locality was decided by iterating a |
Edges carry confidence | A proven same-file call and a same-directory guess were identical evidence. Each edge now scores |
Exported TS symbols indexed once |
|
Method calls on untyped receivers resolve |
|
Centrality is a percentile rank | Scores were divided by the graph maximum, so the top node was |
Resolution is O(1), not O(refs × nodes × files) | Unresolvable references — stdlib and third-party calls, most call sites in real code — paid the worst case. Suffixes are now indexed. |
New capability — concept search. Substring matching cannot find get_authenticated from login; they share no substring. Identifiers are now tokenized and expanded through curated concept clusters, and docstrings, signatures, and paths are indexed alongside names. Deterministic, offline, no model. Available on the library as ArborGraph::search_ranked (arbor query remains literal-substring for now).
New capability — hunk-level impact. changed_node_ids_for_ranges keeps only symbols whose lines actually changed, instead of every symbol in a touched file.
Measured on identical node sets, after the duplicate-extraction fix:
Codebase | Before | After |
TypeScript (149 files) | 172 edges | 196 (+14%) |
Rust (arbor-graph) | 116 edges | 167 (+44%) |
Graph caches from earlier versions are invalidated — centrality now means something different, so a stale cache would be read wrong.
Change | Measured |
PageRank rewrite — flat call-graph adjacency replaces per-iteration traversal | 149.8ms → 6.6ms on a 10k-node graph (23x), verified side-by-side vs the old implementation |
Parallel indexing — parse fans out across all cores, deterministic assembly | Arbor: 253ms → 95ms · tokio (178k LOC): 2.7s → 1.6s |
Warm-start centrality — watcher recomputes seed from previous scores | Converges in ~2 rounds after a one-file patch instead of the full 20-iteration budget |
Convergence early-exit | Iteration stops at 1e-9 max delta — the budget is a ceiling, not a sentence |
Think a number is wrong? cargo bench -p arbor-graph and prove it: BENCHMARKS.md.
Feature | What it does |
MCP | Stateless |
Tasks extension |
|
MCP Apps | Interactive blast-radius graph ( |
HTTP transport |
|
Real | Git-diff-aware impact analysis via shared |
Pagination |
|
Benchmarks | Criterion suite + CI regression gate — see BENCHMARKS.md |
Quickstart
# Install
cargo install arbor-graph-cli
# Index your project (one command)
cd your-project && arbor setup
# Explore before you edit
arbor map . --exclude-test # ranked project skeleton (~1k tokens)
arbor refactor parse_file # blast radius of changing a symbol
arbor diff # impact of uncommitted git changes
# Wire up your AI agent
claude mcp add --transport stdio --scope project arbor -- arbor bridgeAgent workflow: call get_map first → search_symbols / get_file_graph to locate code → Read only the target file. Full MCP guide →
For AI agents (MCP)
Arbor ships a production MCP server via arbor bridge. Stdio is the default; HTTP is opt-in for remote/enterprise.
# Stdio (Claude, Cursor, VS Code)
arbor bridge
# HTTP (MCP 2026-07-28)
arbor bridge --http --port 3333Cursor / VS Code
{
"mcpServers": {
"arbor": {
"type": "stdio",
"command": "arbor",
"args": ["bridge"]
}
}
}Templates: templates/mcp/ · Setup scripts: scripts/setup-mcp.sh · scripts/setup-mcp.ps1
16 MCP tools
Tier | Tools | Use when |
Orientation |
| First call — token-budgeted project skeleton ranked by PageRank |
Surgical |
| Navigate to a specific symbol or file |
Broad |
| Trace dependencies, blast radius, paths |
Agent-native |
| PR impact, onboarding, security audit, bulk lookup |
Every tool returns { ok, tool, data, meta: { suggested_next_tool, suggested_next_args } } so agents chain calls without re-prompting.
Registry: io.github.Anandb71/arbor · Official API lookup · Glama listing
CLI reference
Command | Description |
| One-shot init + index |
| Ranked, token-budgeted project skeleton |
| Fuzzy symbol search (supports |
| One-hop graph traversal |
| HTTP handlers, main, jobs, webhooks |
| Symbols + edges in one file |
| Full symbol detail |
| Shortest call-graph path |
| Blast radius before refactoring |
| Git-change impact report |
| CI safety gate ( |
| Auto-generate PR description |
| Autonomous PR architecture review |
| Codebase onboarding guide |
| Real-time architectural safety gate |
| MCP server (add |
| Live re-index on file changes |
| Native desktop UI |
All query commands support --json. map additionally supports --tokens N, --focus "pattern", --focus-changed.
Visual tour
Full recording: media/recording-2026-01-13.mp4
Installation
# Rust / Cargo
cargo install arbor-graph-cli
# Homebrew (macOS/Linux)
brew install Anandb71/tap/arbor
# Scoop (Windows)
scoop bucket add arbor https://github.com/Anandb71/arbor && scoop install arbor
# npm wrapper (cross-platform)
npx @anandb71/arbor-cli
# Docker
docker pull ghcr.io/anandb71/arbor:latestNo-Rust installers:
macOS/Linux:
curl -fsSL https://raw.githubusercontent.com/Anandb71/arbor/main/scripts/install.sh | bashWindows:
irm https://raw.githubusercontent.com/Anandb71/arbor/main/scripts/install.ps1 | iex
Pinned installs: docs/INSTALL.md
Language support
Production parsers: Rust · TypeScript / JavaScript · Python · Go · Java · C / C++ · C# · Dart
Fallback parsers: Kotlin · Swift · Ruby · PHP · Shell
CI & pull requests
arbor diff --markdown
arbor check --max-blast-radius 30 --markdown
arbor summaryGitHub Action (pre-built binary, ~5s vs ~3–5min compile):
name: Arbor Check
on: [pull_request]
jobs:
arbor:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- uses: Anandb71/arbor@v2.4.0
with:
command: check . --max-blast-radius 30 --markdown
comment-on-pr: true
github-token: ${{ secrets.GITHUB_TOKEN }}Architecture
arbor-core (Tree-sitter parsing)
└── arbor-graph (petgraph + PageRank + impact analysis)
├── arbor-cli — CLI + MCP bridge
├── arbor-mcp — MCP protocol server
├── arbor-server — WebSocket JSON-RPC
├── arbor-watcher — incremental file watcher
└── arbor-gui — desktop UIDocs: Quickstart · Architecture · Graph schema · MCP integration · Benchmarks · Roadmap · Philosophy
Release channels: GitHub Releases · crates.io · GHCR · npm · VS Code / Open VSX · Homebrew · Scoop — Releasing guide
Philosophy
Consumer first — beautiful, intuitive, instantly useful
Accessibility second — works across ecosystems, runs anywhere
Affordability next — minimal overhead, from laptops to monoliths
Arbor is local-first: no mandatory data exfiltration, offline-capable, open source. Security policy →
Contributing
cargo build --workspace
cargo test --workspace
cargo clippy --workspace --all-targets --all-featuresCONTRIBUTING.md · Good first issues · Code of conduct
Contributors
License
MIT — see LICENSE.
Maintenance
Resources
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