How do I install the arbor MCP server?

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "arbor": { "command": "npx", "args": ["-y", "arbor"] } }

The Graph-Native Intelligence Layer for Code.

Is the arbor MCP server free to use?

Yes. arbor is listed on AllMCPs as a free, open Model Context Protocol server you can install into Claude Desktop, Cursor, or any MCP-compatible client.

How do I install the arbor MCP server?

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "arbor": { "command": "npx", "args": ["-y", "arbor"] } }

The Graph-Native Intelligence Layer for Code.

Is the arbor MCP server free to use?

Yes. arbor is listed on AllMCPs as a free, open Model Context Protocol server you can install into Claude Desktop, Cursor, or any MCP-compatible client.

Arbor

Graph-native intelligence for codebases.
Know what breaks before you break it.

Side-by-side: an agent navigating tokio with grep-and-read (47 tool calls, still searching) vs the same agent with arbor's code graph (4 graph calls + 1 read, done)

_{Simulated replay — the arbor commands and their output are real (tokio @ 178k LOC). Methodology: BENCHMARKS.md}

v3.0.0 — The Right Node · v2.6.0 stopped dropping colliding symbols. It did not stop resolving them to the wrong one. When a bare name matched several modules, resolution fell through to "same directory" and confidently attached the edge to whichever definition happened to sit next to the caller. On a graded fixture the three largest hubs reported zero downstream impact while unrelated siblings inherited their centrality. A file's own imports now settle it. Reproduce it yourself: getArbor-dev/arbor-torture

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.

What's new in v3.0.0

One fix, measured.

Symbol resolution consults the importing file. When a bare name matched definitions in several modules, resolve_ref fell through to SameDir and attached the edge to whichever definition sat in the caller's own directory — not a dropped edge, a confidently misrouted one, stamped at 0.55 confidence.

GraphBuilder already kept a per-file import map, but only apply_import_validation read it, and that scores an edge after one has been chosen. It never saw the references going to the wrong node. Consulting it between the same-file and same-directory checks keeps a local definition shadowing an import, while letting a written import beat mere adjacency. Resolution::ViaImport scores 0.93, above SameDir's 0.55.

Measured

A fixture of 260 modules across 10 layers, each layer defining the same 26 function names. Ground truth is derived from the generator's own edge list, so the expected answer is exact rather than estimated.

True downstream	v2.6.0	v3.0.0
179	0	163
178	0	161
161	0	133
143	22	133
122	22	119
36	22	61
16	22	46

Previously flat at about 22 regardless of the real answer. Now it tracks. Risk on the largest hub moves from LOW to CRITICAL.

Total edge count barely moves (1335 → 1334). That is the signature of misrouting rather than loss: the edges were always there, pointing at the wrong nodes.

Breaking

Resolution gains a ViaImport variant — an exhaustive match will not compile
Edges land on different nodes, so cached graphs, stored node ids, and centrality baselines from 2.6.0 will differ

Known and still open

Written down rather than left to be discovered:

Small targets now over-report (36 → 61, 16 → 46). Safer direction than silence, but not yet correct.
PageRank has no escape from a closed cycle. Every member of a 500-function ring scores above 90% centrality on one caller each, so mutually recursive clusters — parsers, tree walkers, state machines — crowd the top of any ranking.
Inheritance produces no edges. class Middle(Base) is invisible, so changing a base class shows zero blast radius.
Dynamic and reflective imports (importlib, __import__, import(), eval(require(...))) are unresolvable by construction and are documented as expected misses in the fixture rather than counted as defects.

v2.6.0 — Ground Truth (colliding symbols kept, deterministic resolution, edge confidence, percentile centrality)

Correctness, not speed. Each of these was silently wrong before.

Fix	Why it mattered
Colliding symbols are kept	`SymbolTable` used `HashMap::insert`, so a second `handler`, `new`, or `process` replaced the first. The loser had zero callers and was invisible to blast radius.
Resolution is deterministic	Same-directory locality was decided by iterating a `HashMap`. Rust seeds `RandomState` per process, so the same binary on the same input could build different edges between runs. Now asserted across eight fresh processes.
Edges carry confidence	A proven same-file call and a same-directory guess were identical evidence. Each edge now scores `[0,1]` by how it resolved.
Exported TS symbols indexed once	`export_statement` recursed into its children, then the generic loop recursed again — every exported symbol became two vertices sharing one node id. 133 phantom nodes on a 149-file app, 25% of the graph.
Method calls on untyped receivers resolve	`obj.method()` was dropped outright, leaving the graph nearly edgeless on TS/JS — and an empty graph reports a blast radius of zero, which reads as "safe" rather than "unknown".
Centrality is a percentile rank	Scores were divided by the graph maximum, so the top node was `1.0` by construction and a `0.6` threshold meant nothing consistent between repos. Adding one hub rescaled every other node.
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.

v2.5.0 — The Last Excuse (PageRank 23x, parallel indexing, warm-start centrality)

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.

v2.4.0 — The Agent-Native Leap (MCP 2026-07-28, HTTP transport, Tasks, MCP Apps)

Feature	What it does
MCP `2026-07-28`	Stateless `server/discover`, response caching (`ttlMs`/`cacheScope`), dual-version fallback for `2025-03-26` clients
Tasks extension	`tasks/get` · `tasks/update` · `tasks/cancel` — cold-start indexing returns task handles, not errors
MCP Apps	Interactive blast-radius graph (`ui://arbor/blast-radius`) and architecture map (`ui://arbor/architecture-map`) inside agent hosts
HTTP transport	`arbor bridge --http --port 3333` — stateless MCP behind load balancers
Real `get_blast_radius`	Git-diff-aware impact analysis via shared `arbor-graph::compute_blast_radius`
Pagination	`offset` / `limit` / `hasMore` on `search_symbols` and `get_map`
Benchmarks	Criterion suite + CI regression gate — see BENCHMARKS.md

Quickstart

bash

# 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 bridge

Agent 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.

bash

# Stdio (Claude, Cursor, VS Code)
arbor bridge

# HTTP (MCP 2026-07-28)
arbor bridge --http --port 3333

Cursor / VS Code

config.json

{
  "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	`get_map`	First call — token-budgeted project skeleton ranked by PageRank
Surgical	`list_entry_points` · `get_callers` · `get_callees` · `search_symbols` · `get_file_graph` · `get_node_detail`	Navigate to a specific symbol or file
Broad	`get_logic_path` · `analyze_impact` · `find_path` · `get_knowledge_path`	Trace dependencies, blast radius, paths
Agent-native	`get_blast_radius` · `explain_symbol` · `audit_security` · `get_architecture_overview` · `batch_query`	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
`arbor setup`	One-shot init + index
`arbor map`	Ranked, token-budgeted project skeleton
`arbor query <term>`	Fuzzy symbol search (supports `\|` OR)
`arbor callers / callees <sym>`	One-hop graph traversal
`arbor entry-points`	HTTP handlers, main, jobs, webhooks
`arbor file-graph <path>`	Symbols + edges in one file
`arbor inspect <sym>`	Full symbol detail
`arbor path <a> <b>`	Shortest call-graph path
`arbor refactor <sym>`	Blast radius before refactoring
`arbor diff`	Git-change impact report
`arbor check`	CI safety gate (`--max-blast-radius N`)
`arbor summary`	Auto-generate PR description
`arbor agent review`	Autonomous PR architecture review
`arbor agent onboard`	Codebase onboarding guide
`arbor agent guard`	Real-time architectural safety gate
`arbor bridge`	MCP server (add `--http` for HTTP transport)
`arbor watch`	Live re-index on file changes
`arbor gui`	Native desktop UI

All query commands support --json. map additionally supports --tokens N, --focus "pattern", --focus-changed.

Visual tour

Arbor visualizer screenshot

Full recording: media/recording-2026-01-13.mp4

Installation

bash

# 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: 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

Adding languages →

CI & pull requests

bash

arbor diff --markdown
arbor check --max-blast-radius 30 --markdown
arbor summary

GitHub Action (pre-built binary, ~5s vs ~3–5min compile):

yaml

name: Arbor Check
on: [pull_request]

jobs:
  arbor:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0

      - uses: getArbor-dev/arbor@v3.0.0
        with:
          command: check . --max-blast-radius 30 --markdown
          comment-on-pr: true
          github-token: ${{ secrets.GITHUB_TOKEN }}

Architecture

Code

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 UI

Docs: 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

bash

cargo build --workspace
cargo test --workspace
cargo clippy --workspace --all-targets --all-features

CONTRIBUTING.md · Good first issues · Code of conduct

Contributors

_{7 contributors | View all}

License

MIT — see LICENSE.

Arbor

Graph-native intelligence for codebases.
Know what breaks before you break it.

Side-by-side: an agent navigating tokio with grep-and-read (47 tool calls, still searching) vs the same agent with arbor's code graph (4 graph calls + 1 read, done)

_{Simulated replay — the arbor commands and their output are real (tokio @ 178k LOC). Methodology: BENCHMARKS.md}

v3.0.0 — The Right Node · v2.6.0 stopped dropping colliding symbols. It did not stop resolving them to the wrong one. When a bare name matched several modules, resolution fell through to "same directory" and confidently attached the edge to whichever definition happened to sit next to the caller. On a graded fixture the three largest hubs reported zero downstream impact while unrelated siblings inherited their centrality. A file's own imports now settle it. Reproduce it yourself: getArbor-dev/arbor-torture

Why Arbor

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.

What's new in v3.0.0

One fix, measured.

Measured

True downstream	v2.6.0	v3.0.0
179	0	163
178	0	161
161	0	133
143	22	133
122	22	119
36	22	61
16	22	46

Previously flat at about 22 regardless of the real answer. Now it tracks. Risk on the largest hub moves from LOW to CRITICAL.

Total edge count barely moves (1335 → 1334). That is the signature of misrouting rather than loss: the edges were always there, pointing at the wrong nodes.

Breaking

Resolution gains a ViaImport variant — an exhaustive match will not compile
Edges land on different nodes, so cached graphs, stored node ids, and centrality baselines from 2.6.0 will differ

Known and still open

Written down rather than left to be discovered:

Small targets now over-report (36 → 61, 16 → 46). Safer direction than silence, but not yet correct.
PageRank has no escape from a closed cycle. Every member of a 500-function ring scores above 90% centrality on one caller each, so mutually recursive clusters — parsers, tree walkers, state machines — crowd the top of any ranking.
Inheritance produces no edges. class Middle(Base) is invisible, so changing a base class shows zero blast radius.
Dynamic and reflective imports (importlib, __import__, import(), eval(require(...))) are unresolvable by construction and are documented as expected misses in the fixture rather than counted as defects.

v2.6.0 — Ground Truth (colliding symbols kept, deterministic resolution, edge confidence, percentile centrality)

Correctness, not speed. Each of these was silently wrong before.

Fix	Why it mattered
Colliding symbols are kept	`SymbolTable` used `HashMap::insert`, so a second `handler`, `new`, or `process` replaced the first. The loser had zero callers and was invisible to blast radius.
Resolution is deterministic	Same-directory locality was decided by iterating a `HashMap`. Rust seeds `RandomState` per process, so the same binary on the same input could build different edges between runs. Now asserted across eight fresh processes.
Edges carry confidence	A proven same-file call and a same-directory guess were identical evidence. Each edge now scores `[0,1]` by how it resolved.
Exported TS symbols indexed once	`export_statement` recursed into its children, then the generic loop recursed again — every exported symbol became two vertices sharing one node id. 133 phantom nodes on a 149-file app, 25% of the graph.
Method calls on untyped receivers resolve	`obj.method()` was dropped outright, leaving the graph nearly edgeless on TS/JS — and an empty graph reports a blast radius of zero, which reads as "safe" rather than "unknown".
Centrality is a percentile rank	Scores were divided by the graph maximum, so the top node was `1.0` by construction and a `0.6` threshold meant nothing consistent between repos. Adding one hub rescaled every other node.
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 — 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.

v2.5.0 — The Last Excuse (PageRank 23x, parallel indexing, warm-start centrality)

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.

v2.4.0 — The Agent-Native Leap (MCP 2026-07-28, HTTP transport, Tasks, MCP Apps)

Feature	What it does
MCP `2026-07-28`	Stateless `server/discover`, response caching (`ttlMs`/`cacheScope`), dual-version fallback for `2025-03-26` clients
Tasks extension	`tasks/get` · `tasks/update` · `tasks/cancel` — cold-start indexing returns task handles, not errors
MCP Apps	Interactive blast-radius graph (`ui://arbor/blast-radius`) and architecture map (`ui://arbor/architecture-map`) inside agent hosts
HTTP transport	`arbor bridge --http --port 3333` — stateless MCP behind load balancers
Real `get_blast_radius`	Git-diff-aware impact analysis via shared `arbor-graph::compute_blast_radius`
Pagination	`offset` / `limit` / `hasMore` on `search_symbols` and `get_map`
Benchmarks	Criterion suite + CI regression gate — see BENCHMARKS.md

Quickstart

bash

# 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 bridge

Agent 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.

bash

# Stdio (Claude, Cursor, VS Code)
arbor bridge

# HTTP (MCP 2026-07-28)
arbor bridge --http --port 3333

Cursor / VS Code

config.json

{
  "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	`get_map`	First call — token-budgeted project skeleton ranked by PageRank
Surgical	`list_entry_points` · `get_callers` · `get_callees` · `search_symbols` · `get_file_graph` · `get_node_detail`	Navigate to a specific symbol or file
Broad	`get_logic_path` · `analyze_impact` · `find_path` · `get_knowledge_path`	Trace dependencies, blast radius, paths
Agent-native	`get_blast_radius` · `explain_symbol` · `audit_security` · `get_architecture_overview` · `batch_query`	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
`arbor setup`	One-shot init + index
`arbor map`	Ranked, token-budgeted project skeleton
`arbor query <term>`	Fuzzy symbol search (supports `\|` OR)
`arbor callers / callees <sym>`	One-hop graph traversal
`arbor entry-points`	HTTP handlers, main, jobs, webhooks
`arbor file-graph <path>`	Symbols + edges in one file
`arbor inspect <sym>`	Full symbol detail
`arbor path <a> <b>`	Shortest call-graph path
`arbor refactor <sym>`	Blast radius before refactoring
`arbor diff`	Git-change impact report
`arbor check`	CI safety gate (`--max-blast-radius N`)
`arbor summary`	Auto-generate PR description
`arbor agent review`	Autonomous PR architecture review
`arbor agent onboard`	Codebase onboarding guide
`arbor agent guard`	Real-time architectural safety gate
`arbor bridge`	MCP server (add `--http` for HTTP transport)
`arbor watch`	Live re-index on file changes
`arbor gui`	Native desktop UI

All query commands support --json. map additionally supports --tokens N, --focus "pattern", --focus-changed.

Visual tour

Arbor visualizer screenshot

Full recording: media/recording-2026-01-13.mp4

Installation

bash

# 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: 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

Adding languages →

CI & pull requests

bash

arbor diff --markdown
arbor check --max-blast-radius 30 --markdown
arbor summary

GitHub Action (pre-built binary, ~5s vs ~3–5min compile):

yaml

name: Arbor Check
on: [pull_request]

jobs:
  arbor:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0

      - uses: getArbor-dev/arbor@v3.0.0
        with:
          command: check . --max-blast-radius 30 --markdown
          comment-on-pr: true
          github-token: ${{ secrets.GITHUB_TOKEN }}

Architecture

Code

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 UI

Docs: 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

bash

cargo build --workspace
cargo test --workspace
cargo clippy --workspace --all-targets --all-features

CONTRIBUTING.md · Good first issues · Code of conduct

Contributors

_{7 contributors | View all}

License

MIT — see LICENSE.

Arbor

Quick Install

Install Config Generator

Documentation Overview

Arbor

Why Arbor

What's new in v3.0.0

Measured

Breaking

Known and still open

Quickstart

For AI agents (MCP)

Cursor / VS Code

16 MCP tools

CLI reference

Visual tour

Installation

Language support

CI & pull requests

Architecture

Philosophy

Contributing

Contributors

License

Related MCP Servers

Frequently Asked Questions about Arbor

AllMCPs Directory Badge

Technical Specs & Signals

Feature Your MCP Server

Own this project?

Share & Embed

Explore more

Arbor

Quick Install

Install Config Generator

Documentation Overview

Arbor

Why Arbor

What's new in v3.0.0

Measured

Breaking

Known and still open

Quickstart

For AI agents (MCP)

Cursor / VS Code

16 MCP tools

CLI reference

Visual tour

Installation

Language support

CI & pull requests

Architecture

Philosophy

Contributing

Contributors

License

Related MCP Servers

Frequently Asked Questions about Arbor

AllMCPs Directory Badge

Technical Specs & Signals

Feature Your MCP Server

Own this project?

Share & Embed

Explore more