Arbor代码智能分析
Arbor代码智能分析 是 AI Skill Hub 本期精选MCP工具之一。综合评分 8.2 分，整体质量较高。我们强烈推荐将其纳入你的 AI 工具库，帮助提升工作效率。
📚 深度解析
通过安装 Arbor代码智能分析，你的 AI 助手将获得额外的工具调用能力，可以用自然语言直接操控该工具的功能，无需学习复杂的命令行语法。MCP 工具的核心价值在于"一次配置，永久增强"——配置完成后，每次与 AI 对话时都可以无缝调用这些工具。
在技术实现上，MCP 工具通过标准的 JSON-RPC 协议与 AI 客户端通信，工具的功能以"工具列表"的形式暴露给 AI 模型，AI 可以按需调用。Arbor代码智能分析 提供了结构化的工具调用接口，使 AI 模型能够精确地理解和使用每个功能点，显著降低 AI 在工具使用上的错误率。
与传统的 API 集成相比，MCP 工具的优势在于无需编写代码——用户只需在配置文件中添加几行 JSON，即可让 AI 获得全新能力。AI Skill Hub 将 Arbor代码智能分析 评为 AI 评分 8.2 分，属于同类工具中的优质选择。
📋 工具概览
基于图神经网络的开源代码智能工具，用AST替代嵌入式RAG实现确定性代码分析。提供精确的代码理解和智能补全，适合开发者、代码审查和AI应用集成场景。
Arbor代码智能分析 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
📖 中文文档
基于图神经网络的开源代码智能工具，用AST替代嵌入式RAG实现确定性代码分析。提供精确的代码理解和智能补全，适合开发者、代码审查和AI应用集成场景。
Arbor代码智能分析 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
- 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
- 提供结构化工具调用接口，显著降低 AI 集成复杂度
- 支持 Claude Desktop 和 Claude Code 无缝接入，开箱即用
- 可与其他 MCP 工具组合叠加，构建完整 AI 工作站
- 轻量无侵入设计，不影响现有系统架构
- 在 Claude Desktop 对话中直接调用本地工具，实现 AI 与系统的深度联动
- 通过自然语言驱动复杂的多步骤自动化任务，代替繁琐手动操作
- 将多个 MCP 工具组合使用，构建个人专属 AI 工作站
# 方式一：通过 Claude Code CLI 一键安装
claude skill install https://github.com/Anandb71/arbor
# 方式二：手动配置 claude_desktop_config.json
{
"mcpServers": {
"arbor------": {
"command": "npx",
"args": ["-y", "arbor"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
- 确认已安装 Node.js（v18 或以上版本）
- 打开 Claude Desktop 或 Claude Code 的 MCP 配置文件
- 按「交给 Agent 安装 → Claude Desktop」标签中的 JSON 配置填入 mcpServers 字段
- 保存配置文件并重启 Claude 客户端
- 重启后，在对话中即可使用本工具
# 安装后在 Claude 对话中直接使用 # 示例： 用户: 请帮我用 Arbor代码智能分析 执行以下任务... Claude: [自动调用 Arbor代码智能分析 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入："列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"arbor______": {
"command": "npx",
"args": ["-y", "arbor"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
简介
<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="docs/assets/arbor-lockup-dark.svg" /> <source media="(prefers-color-scheme: light)" srcset="docs/assets/arbor-lockup-light.svg" /> <img src="docs/assets/arbor-lockup-dark.svg" alt="Arbor" width="300" /> </picture> </p>
<p align="center"> <strong>Code context for people and agents.</strong><br> Find a function’s callers. Trace the path through your code.<br> Know what breaks <em>before</em> you break it. </p>
<p align="center"> <a href="https://github.com/Anandb71/arbor/actions"><img src="https://img.shields.io/github/actions/workflow/status/Anandb71/arbor/rust.yml?style=flat-square&label=Rust%20CI" alt="Rust CI" /></a> <a href="https://crates.io/crates/arbor-graph-cli"><img src="https://img.shields.io/crates/v/arbor-graph-cli?style=flat-square&label=crates.io" alt="Crates.io" /></a> <a href="https://github.com/Anandb71/arbor/releases"><img src="https://img.shields.io/github/v/release/Anandb71/arbor?style=flat-square&label=release" alt="Latest release" /></a> <a href="https://github.com/Anandb71/arbor/pkgs/container/arbor"><img src="https://img.shields.io/badge/GHCR-container-blue?style=flat-square" alt="GHCR" /></a> <a href="https://glama.ai/mcp/servers/@Anandb71/arbor"><img src="https://img.shields.io/badge/MCP%20Directory-Glama-6f42c1?style=flat-square" alt="Glama MCP Directory" /></a> <img src="https://img.shields.io/badge/license-MIT-green?style=flat-square" alt="MIT License" /> </p>
<p align="center"> <img src="docs/assets/arbor-demo.gif" alt="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)" width="900" /> </p> <p align="center"> <sub>Simulated replay — the <code>arbor</code> commands and their output are real (tokio @ 178k LOC). Methodology: <a href="docs/BENCHMARKS.md">BENCHMARKS.md</a></sub> </p>
v3.1.0 · Find code that calls retiring AI models:arbor retirementslists the direct OpenAI, Anthropic and Google SDK calls in a Python project that use a retired or retiring model, with the listed replacement and what has to change, locally or as a GitHub Action.arbor usagesand the MCP toolfind_api_usageslist every such SDK call with its routing and model. Release notes
---
What's new in v3.1.0
A feature release: two commands for Python code that calls AI model APIs. Both run locally, with no account, API key or telemetry.
| New | What it does |
|---|---|
**arbor retirements** | Lists the direct openai, anthropic and Google SDK calls that use a model its provider has retired or is retiring: the date, the replacement the provider lists, the arguments that change with it and the choices your repo makes first. OpenAI shuts down gpt-4o, gpt-4o-mini, gpt-4.1 and gpt-4.1-mini on 14 January 2027. [More below](#find-code-that-calls-retiring-ai-models) |
**GitHub Action mode: retirements** | The same check in CI: a job summary that lists every affected call, and warnings on the most urgent lines. It warns by default and fails the build only when you set retirements-fail-on, or when the check cannot run. |
**arbor usages and find_api_usages** | Every OpenAI, Anthropic and Gemini SDK call with its file and line, client routing (direct, Azure, custom base_url, Bedrock, Vertex) and model IDs, for people and agents. A value is confirmed only inside a small supported grammar; everything else carries a typed gap. [What a report confirms](docs/SDK_USAGES.md) |
Python only for now; graph extraction is unchanged (a saved graph is rebuilt once on first use, as after every upgrade). See the 3.0.5 release notes for the previous release.
<details> <summary><strong>v3.0.3</strong> (Rust calls through paths and macros, per-symbol diff, <code>--base</code>)</summary>
A fix release. Every item below was a wrong answer, not a missing feature, and each ships with a regression test.
| Fix | What was wrong |
|---|---|
| **Rust calls resolve** | crate::jobs::enqueue(), Type::new(), self.helper() and calls inside assert!/format! produced no edge, so heavily used Rust functions reported no callers. Paths now resolve through the module tree, and macro arguments are read as code. |
**Per-symbol arbor diff** | Every symbol in a touched file counted as changed, so adding a function to a busy file reported that file's whole blast radius. New symbols now carry none; only modified ones do. Tests that call the change are listed as worth running, not counted as impact. |
**--base and --staged** | arbor diff, check and summary saw only uncommitted edits unless ARBOR_DIFF_BASE was set. --base origin/main compares against the merge base, which is what a pull request shows; --staged checks just what is staged. |
| **Languages stay apart** | A TypeScript enqueue could be reported as a caller of a Rust enqueue. Resolution stays within a language family, and callers/callees list each same-named definition separately. |
| **No stale graphs** | After a branch switch, answers came from the old branch's graph when no remaining file was newer than it. The graph now records the commit it was built from and refreshes when HEAD moves. |
| **Inheritance edges** | class Middle(Base) produced no edge, so changing a base class showed zero blast radius. extends/implements edges are emitted and inherited methods stay reachable. |
| **Call cycles rank as one** | A closed ring of functions filled the top of every centrality ranking. Cycles are condensed before PageRank. |
| **HTTP bridge hardening** | arbor bridge --http accepted cross-origin and DNS-rebinding requests. It now checks Origin/Host and requires JSON bodies. |
| **Quieter bridge** | The bridge re-indexed ignored build directories in a busy loop. |
Also new: arbor receipt. After each coding-agent turn it explains, in plain English, what changed and what was touched that you didn't ask for, with arbor receipt undo to put a turn back.
Cached graphs from 3.0.0 are rebuilt automatically on first use.
</details>
<details> <summary><strong>v3.0.0 — The Right Node</strong> (symbol resolution consults the importing file)</summary>
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.
Install (crates.io can lag the latest release: see docs/INSTALL.md)
cargo install arbor-graph-cli
Installation
```bash
Docker
docker pull ghcr.io/anandb71/arbor:latest ```
Pinned installs: docs/INSTALL.md
---
Quickstart
```bash
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. Same-named symbols are listed per definition; narrow with jobs::enqueue, Type.method or src/jobs.rs:enqueue |
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 usages | OpenAI, Anthropic and Gemini SDK calls in Python: client routing (direct, Azure, custom base_url, Bedrock, Vertex), model IDs and typed gaps; never prints credentials. --sdk openai\|anthropic\|google, --json. What a value with no gap confirms: [docs/SDK_USAGES.md](docs/SDK_USAGES.md) |
arbor retirements | Direct SDK calls to AI models their provider has retired or is retiring (Python): date and days left, listed replacement, the argument changes and per-repo choices it needs. --fail-within <days>, --count-unconfirmed, --include-routed, --json, --markdown, --annotations ([guide](docs/RETIREMENTS.md)) |
arbor diff | Git-change impact report, per symbol: new symbols carry no blast radius. --base <ref> compares against the merge base (what a PR shows), --staged only staged changes |
arbor check | CI safety gate (--max-blast-radius N, --base <ref>) |
arbor summary | Auto-generate PR description (--base <ref>) |
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 receipt list / show / undo | Plain-English receipts of what each agent turn changed, and undo for a turn ([guide](docs/RECEIPTS.md)) |
arbor hook claude | Wire Arbor into Claude Code: directives, receipts after every turn |
arbor gui | Native desktop UI |
All query commands support --json. map additionally supports --tokens N, --focus "pattern", --focus-changed.
---
Stdio (Claude, Cursor, VS Code)
arbor bridge
Cursor / VS Code
{
"mcpServers": {
"arbor": {
"type": "stdio",
"command": "arbor",
"args": ["bridge"]
}
}
}Templates: templates/mcp/ · Setup scripts: scripts/setup-mcp.sh · scripts/setup-mcp.ps1
创新性强，以确定性图分析替代概率RAG，技术方向正确。代码质量好，Rust实现高效稳定，有望成为代码智能领域标杆工具。
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- 容器内无法访问宿主机 localhost — 使用 host.docker.internal
- Docker：arbor 提供官方镜像，docker compose up 一键启动
- CLI：直接 npm install -g / pip install，命令行调用
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
- 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
- 提供结构化工具调用接口，显著降低 AI 集成复杂度
- 支持 Claude Desktop 和 Claude Code 无缝接入，开箱即用
- 可与其他 MCP 工具组合叠加，构建完整 AI 工作站
- 轻量无侵入设计，不影响现有系统架构
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- 容器内无法访问宿主机 localhost — 使用 host.docker.internal
👥 适合人群
🎯 使用场景
- 在 Claude Desktop 对话中直接调用本地工具，实现 AI 与系统的深度联动
- 通过自然语言驱动复杂的多步骤自动化任务，代替繁琐手动操作
- 将多个 MCP 工具组合使用，构建个人专属 AI 工作站
⚖️ 优点与不足
- +MIT 协议，可免费商用
- +标准化 MCP 协议，生态互联性强
- +与 Claude 官方生态无缝对接
- +即插即用，配置简单快捷
- −依赖 Claude 客户端，非 Claude 用户无法使用
- −MCP 协议仍在持续演进，接口可能变更
- −需要一定的配置步骤
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一，可自由商用、修改、分发，仅需保留版权声明。
🔗 相关工具推荐
❓ 常见问题 FAQ
经综合评估，Arbor代码智能分析 在MCP工具赛道中表现稳健，质量优秀。如果你已有明确的使用需求，可以直接上手体验；如果还在评估阶段，建议对比同类工具后再做决策。
| 原始名称 | arbor |
| 原始描述 | 开源MCP工具：Graph-native code intelligence that replaces embedding-based RAG with determinis。⭐117 · Rust |
| Topics | 代码分析图神经网络AST解析MCP协议Rust工具 |
| GitHub | https://github.com/Anandb71/arbor |
| License | MIT |
| 语言 | Rust |
收录时间：2026-05-19 · 更新时间：2026-05-30 · License：MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。