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Arbor

Graph-native intelligence for codebases.

Know what breaks before you break it.

Table of Contents

Related MCP server: CodeGraph CLI MCP Server

The Arbor Philosophy

Arbor is rooted in three unwavering principles, listed in strict order of priority. Every architectural decision is measured against this hierarchy:

  1. Consumer First: Tooling must be beautiful, intuitive, and instantly useful out of the box. The developer experience triumphs over all other metrics.

  2. Accessibility Second: Deep AI intelligence and graph analysis should never be gatekept. Our tooling is built to work seamlessly across language ecosystems and run deterministically on any machine.

  3. Affordability Next: We ruthlessly optimize for minimal computational overhead. From edge laptops to giant monoliths, graph exploration should have zero-friction adoption.

For comprehensive details on our approach, read our PHILOSOPHY.md.

Why Arbor

Most AI code tooling treats code as text retrieval.

Arbor builds a semantic dependency graph and answers execution-aware questions:

  • If I change this symbol, what breaks?

  • Who calls this function, directly and transitively?

  • What is the shortest architectural path between these two nodes?

You get deterministic, explainable impact analysis instead of approximate keyword matches.

What you get

  • Blast radius analysis: See exactly which files and modules will be affected by a change (complete with depth confidence levels) before you ever press save.

  • Graph-backed symbol resolution: Accurately tracks dependencies across files and entire language boundaries automatically.

  • Agent-native MCP (v2.1.0): 10 MCP tools — surgical traversal (get_callers, get_callees, list_entry_points, search_symbols, get_file_graph, get_node_detail) plus broad analysis tools, all returning a suggested_next_tool hint for zero-reprompt agent chaining.

  • Unified Tooling (CLI + GUI + MCP): Native desktop GUI, a blazing fast CLI, and Claude/AI Model Context Protocol integration all utilizing the exact same core analytical reasoning engine.

  • Git-aware risk gating: Block pull-requests automatically in your CI/CD if a PR introduces a dangerously high architectural blast radius.

  • Lightning fast incremental indexing: Sub-second background cache updates instantly tracking your code edits in real-time.

Visual tour

For a full-screen recording of the workflow, see media/recording-2026-01-13.mp4.

Quickstart

# 1) Install the Arbor CLI globally via Cargo
cargo install arbor-graph-cli

# 2) Initialize Arbor and build the dependency graph for your codebase
cd your-project
arbor setup

# 3) See EVERYTHING a function touches before you break it
arbor refactor <symbol-name>

# 4) Run safety checks (Great for CI/CD or before committing)
arbor diff  # See what your uncommitted git changes impact
arbor check --max-blast-radius 30  # Fail the checks if your changes break more than 30 nodes

# 5) Launch the visual interface to intuitively explore your code's architecture
arbor gui

Installation options

Use whichever channel fits your environment:

# 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:latest

No-Rust installers:

  • macOS/Linux: curl -fsSL https://raw.githubusercontent.com/Anandb71/arbor/main/scripts/install.sh | bash

  • Windows PowerShell: irm https://raw.githubusercontent.com/Anandb71/arbor/main/scripts/install.ps1 | iex

For pinned/versioned installs, see docs/INSTALL.md.

MCP integration

Arbor includes a real MCP server via arbor bridge (stdio transport). v2.1.0 adds 10 tools — 6 surgical tools for precise graph traversal and 4 broad tools for architectural analysis.

Claude Code quick install

claude mcp add --transport stdio --scope project arbor -- arbor bridge
claude mcp list

Available tools

Surgical (v2.1.0): list_entry_points · get_callers · get_callees · search_symbols · get_file_graph · get_node_detail

Broad: get_logic_path · analyze_impact · find_path · get_knowledge_path

All tools return a standard envelope with suggested_next_tool + suggested_next_args so agents can chain calls without re-prompting.

Multi-client setup

Registry verification (authoritative)

NOTE

github.com/mcp search UI may lag indexing. Use the official registry API lookup above as source of truth.

Language support

Arbor supports production parsing and graph analysis across major ecosystems:

  • Rust

  • TypeScript / JavaScript

  • Python

  • Go

  • Java

  • C / C++

  • C# (Native Tree-sitter)

  • Dart

  • Kotlin (fallback parser)

  • Swift (fallback parser)

  • Ruby (fallback parser)

  • PHP (fallback parser)

  • Shell (fallback parser)

Detailed parser notes and expansion guidance:

Architecture and docs

Start here when you need deeper internals:

Git-aware CI workflows

Arbor supports pre-merge risk checks and change gating:

arbor diff
arbor check --max-blast-radius 30
arbor open <symbol>

Use the repository GitHub Action for CI integration:

name: Arbor Check
on: [pull_request]

jobs:
  arbor:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: Anandb71/arbor@v2.0.1
        with:
          command: check . --max-blast-radius 30

Release channels

Automated release distribution includes:

  • GitHub Releases (platform binaries)

  • crates.io

  • GHCR container images

  • npm wrapper package

  • VS Code Marketplace / Open VSX extension channels

  • Homebrew + Scoop

Runbook: docs/RELEASING.md

Contributing

Contributions are welcome.

For local development:

cargo build --workspace
cargo test --workspace

Contributors

Security

Arbor is local-first by design:

  • No mandatory data exfiltration

  • Offline-capable workflows

  • Open-source code paths

Report vulnerabilities via SECURITY.md.

License

MIT — see LICENSE.

Available Tools

2 tools
analyze_impactC

Analyzes the impact (blast radius) of changing a specific node.

ParametersJSON Schema
NameRequiredDescriptionDefault
node_idYesID or name of the node to analyze

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions analyzing impact but doesn't specify what the analysis entails (e.g., computational cost, side effects, permissions required, or output format). This leaves significant gaps in understanding the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It is appropriately sized and front-loaded, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of impact analysis, no annotations, and no output schema, the description is incomplete. It doesn't explain what 'blast radius' entails, the nature of the analysis, or what results to expect, leaving the agent with insufficient context for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with 'node_id' documented as 'ID or name of the node to analyze'. The description adds no additional parameter semantics beyond this, so it meets the baseline of 3 for high schema coverage without extra value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Analyzes the impact (blast radius) of changing a specific node.' It specifies the verb ('analyzes') and resource ('impact of changing a specific node'), though it doesn't explicitly differentiate from the sibling tool 'get_logic_path' (which might retrieve paths rather than analyze impact).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, such as the sibling tool 'get_logic_path'. It lacks context on prerequisites, scenarios where this analysis is needed, or any exclusions, leaving the agent without usage direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_logic_pathC

Traces the call graph to find dependencies and usage of a function or class.

ParametersJSON Schema
NameRequiredDescriptionDefault
start_nodeYesName of the function or class to trace

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions tracing and finding dependencies/usage, which suggests a read-only analysis operation, but doesn't clarify if it's safe, has side effects, requires permissions, or details output format (e.g., graph structure, depth limits). This leaves significant gaps for a tool with potential complexity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description is incomplete for a tool that traces call graphs. It lacks details on behavioral traits (e.g., safety, performance), output format, or how it differs from siblings, making it inadequate for an agent to fully understand usage without additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, clearly documenting the single parameter 'start_node'. The description adds context by specifying it traces 'dependencies and usage of a function or class', which aligns with the schema but doesn't provide additional syntax or format details beyond what's already covered.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('traces', 'find') and resources ('call graph', 'dependencies and usage', 'function or class'), making it easy to understand what it does. However, it doesn't explicitly differentiate from its sibling tool 'analyze_impact', which might have overlapping or related functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus its sibling 'analyze_impact' or any alternatives. It implies usage for tracing dependencies and usage, but lacks explicit context, prerequisites, or exclusions, leaving the agent to infer appropriate scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv1.0.0
    • First observedanalyze_impact
    • First observedget_logic_path
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TDQS

B3.1/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: analyze_impact focuses on assessing the blast radius of a node change, while get_logic_path traces dependencies and usage in a call graph. There is no overlap or ambiguity between these functions.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern (analyze_impact, get_logic_path) with clear, descriptive names. The naming style is uniform and predictable across the set.

Tool Count2/5

With only two tools, the server feels thin for a domain like code or system analysis, where more operations (e.g., for managing nodes, viewing graphs, or updating logic) might be expected. This limited set could hinder agent workflows.

Completeness2/5

Inferring a domain of code or system dependency analysis, the toolset is severely incomplete. It lacks basic CRUD operations (e.g., create, update, delete nodes) and essential functions like listing or searching dependencies, leaving significant gaps for agent tasks.

Maintenance

ActivityMaintained
ResponsivenessResponsive

Resources

Unclaimed servers have limited discoverability.

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