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铁爪
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MCP工具

铁爪

基于 Go · 让 AI 助手直接操作你的系统与工具
英文名:ironclaw
⭐ 8 Stars 🍴 1 Forks 💻 Go 📄 AGPL-3.0 🏷 AI 8.0分
8.0AI 综合评分
ai-agentsai-assistantgolang
✦ AI Skill Hub 推荐

铁爪 是 AI Skill Hub 本期精选MCP工具之一。综合评分 8.0 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

铁爪 是一款基于 MCP(Model Context Protocol)标准协议的 AI 工具扩展。MCP 协议由 Anthropic 开发并开源,旨在建立 AI 模型与外部工具之间的标准化通信接口,目前已被 Claude Desktop、Claude Code、Cursor 等主流 AI 工具采纳。

通过安装 铁爪,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。铁爪 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 铁爪 评为 AI 评分 8.0 分,属于同类工具中的优质选择。

📋 工具概览

铁爪 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。

GitHub Stars
⭐ 8
开发语言
Go
支持平台
Windows / macOS / Linux(跨平台)
维护状态
轻量级项目,按需更新
开源协议
AGPL-3.0
AI 综合评分
8.0 分
工具类型
MCP工具
Forks
1

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

铁爪 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。

📌 核心特色
  • 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
🎯 主要使用场景
  • 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
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/IronSecCo/ironclaw

# 方式二:手动配置 claude_desktop_config.json
{
  "mcpServers": {
    "--": {
      "command": "npx",
      "args": ["-y", "ironclaw"]
    }
  }
}

# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
📋 安装步骤说明
  1. 确认已安装 Node.js(v18 或以上版本)
  2. 打开 Claude Desktop 或 Claude Code 的 MCP 配置文件
  3. 按「交给 Agent 安装 → Claude Desktop」标签中的 JSON 配置填入 mcpServers 字段
  4. 保存配置文件并重启 Claude 客户端
  5. 重启后,在对话中即可使用本工具
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 安装后在 Claude 对话中直接使用
# 示例:
用户: 请帮我用 铁爪 执行以下任务...
Claude: [自动调用 铁爪 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"
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
// claude_desktop_config.json 配置示例
{
  "mcpServers": {
    "__": {
      "command": "npx",
      "args": ["-y", "ironclaw"],
      "env": {
        // "API_KEY": "your-api-key-here"
      }
    }
  }
}

// 保存后重启 Claude Desktop 生效
📑 README 深度解析 真实文档 完整度 74/100 含工作流图 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

简介

<img src="docs/assets/logo.svg" alt="IronClaw" width="380">

See what's waiting for approval, then approve or reject by id

ironctl change pending ironctl change approve <change-id> --by alice

Prerequisites

RequirementForNotes
**Go 1.23+ and a C toolchain**building everythingCGO_ENABLED=1 is required — the encrypted-SQLite binding builds via cgo
**containerd + gVisor (runsc)**production sandboxingruntime io.containerd.runsc.v1; not needed for --dev
**Tailscale**remote admin accessthe control-plane API binds to the tailnet IP; no public port
**SQLCipher (vendored)**encrypted queuesthe SQLCipher C amalgamation is vendored by the driver; no system lib needed
**A model credential**live model callsan Anthropic / OpenAI / OpenRouter key, or a gateway like OneCLI — injected host-side into the model proxy, never into the sandbox ([Model providers](#model-providers))

The three external runtime dependencies (gVisor, Tailscale, the encrypted-SQLite binding) are intentionally not vendored. See deploy/README.md for host setup.

1. Install — detects your OS/arch and verifies the SHA-256 checksum before installing

curl -fsSL https://raw.githubusercontent.com/IronSecCo/ironclaw/main/scripts/install.sh | sh

Installation

Install system-wide (a normal user defaults to ~/.local/bin)

curl -fsSL https://raw.githubusercontent.com/IronSecCo/ironclaw/main/scripts/install.sh | sudo sh

Choose the install directory

curl -fsSL https://raw.githubusercontent.com/IronSecCo/ironclaw/main/scripts/install.sh | IRONCLAW_BINDIR="$HOME/bin" sh


Then confirm what you installed:
sh ironctl --version ```

Prefer to grab files by hand? Download the archive and SHA256SUMS for your platform from the latest release.

Build all binaries

make build # == go build ./...

Or install the two host binaries onto your PATH

go build -o /usr/local/bin/ironclaw-controlplane ./cmd/controlplane go build -o /usr/local/bin/ironctl ./cmd/ironctl ```

For a full system install — build and install the binaries, provision /etc/ironclaw and /var/lib/ironclaw, and enable the service (systemd on Linux, launchd on macOS) — run sudo deploy/install.sh. It needs root to write under /etc and /var/lib. The external runtime dependencies it relies on (containerd + gVisor and Tailscale) are set up separately — see deploy/README.md.

With Docker (`docker compose`)

Self-host the control-plane in one command. From a clone:

cp .env.example .env          # fill in ANTHROPIC_API_KEY (optional to boot)
docker compose up -d          # builds locally on first run, or pulls the GHCR image
docker compose logs -f controlplane   # CLAIM the admin token printed once on first run

The admin/API token is minted on first run and printed once in the logs (there is no recovery) unless you set IRONCLAW_API_TOKEN yourself. The admin API is published on 127.0.0.1:8787 only — front it with Tailscale for remote access.

Prefer the published image? It is pushed to GitHub Container Registry on every release:

```sh docker pull ghcr.io/ironsecco/ironclaw-controlplane:latest

or pin a release: docker pull ghcr.io/ironsecco/ironclaw-controlplane:v0.1.0

```

Set IRONCLAW_IMAGE in .env to pin that tag for docker compose. Every variable the control-plane reads is documented in .env.example. The agent sandboxes themselves are not compose services — the control-plane launches them as gVisor (runsc) children with network=none; running real sandboxes needs a runsc-capable host (see deploy/README.md).

Going to production? The deployment guide covers the hardened, durable posture: locked-down deploy/docker-compose.prod.yml (read-only rootfs, dropped caps, resource limits) behind a TLS reverse proxy (deploy/Caddyfile), secrets via an env-file, encrypted-state backup/restore, pinned-digest upgrades, and Prometheus /metrics.

Quickstart

A fuller local walkthrough — run the control-plane from source in dev mode (no gVisor, binds to loopback) and drive it with the admin CLI:

```sh

Examples

Runnable recipes live in examples/ — each is a directory with a README.md and a setup.sh. Three of them ship a run-mock.sh that drives the whole inbound → agent → reply pipeline on the offline mock provider, so a fresh clone runs them with no model key and no channel tokens:

docker compose -f docker-compose.demo.yml up -d --build   # seeds the offline mock-agent
./examples/scheduled-report/run-mock.sh                   # cron-style self-scheduling summary
./examples/webhook-responder/run-mock.sh                  # inbound webhook → agent reply
./examples/slack-triage/run-mock.sh                       # classify/label every message
  • scheduled-report/ — wakes itself on a schedule (schedule_task), summarizes, posts to a channel. (credential-free demo)
  • webhook-responder/ — routes an inbound HTTP webhook to an agent that replies. (credential-free demo)
  • slack-triage/ — classifies/labels every incoming Slack message. (credential-free demo)
  • personal-assistant/ — a private 1:1 assistant on Telegram, plus a walk-through of the mandatory change-approval flow.
  • channel-triage/ — a Slack triage bot that engages only on @mention, only for known senders.
  • multi-agent-team/ — two agents sharing one channel, separated by engage mode and priority.

Usage

Guided wizard (run in a terminal with no flags): name → template → persona → tools → confirm

ironctl agent create

Configuration

- State lives under --state-dir: the durable gateway change store (survives restart), the append-only JSONL audit log, and the host keystore. - Secrets are host-only. The model credential (an Anthropic / OpenAI / OpenRouter key, or a credential gateway like OneCLI — see Model providers) is applied to outbound model calls by the host modelproxy; the sandbox never sees it and has network=none. Per-session 256-bit keys are generated and held by the host and handed to the sandbox via tmpfs at launch — never via an env var, never baked into the image. - Mesh. Bind --api-addr to the Tailscale interface and firewall the API port on every other interface. See deploy/README.md.

2. Start the control-plane in dev mode — API base URL: http://127.0.0.1:8787

export IRONCLAW_API_TOKEN=$(openssl rand -hex 32) ironclaw-controlplane --dev --api-addr 127.0.0.1:8787 &

CLI-first and API-first

This is a feature, not a missing dashboard. Every capability is a documented HTTP endpoint and an ironctl subcommand, so IronClaw is scriptable, auditable, and CI-friendly from the first command — with no public web surface to phish, misconfigure, or leave exposed. (There is now a private, mesh-only web console at /ui/ — but it's additive, never the only way in, and rides the same Tailscale-bound API, so it adds no public port.)

---

Control-plane HTTP API

Method & pathPurpose
GET /healthzliveness (unauthenticated)
POST /v1/changessubmit a ChangeRequest
GET /v1/changes/pendinglist pending changes
GET /v1/changes/historylist all changes
POST /v1/changes/{id}/decisionrecord an approve/reject decision
GET /v1/auditread the audit log

Via a credential gateway like OneCLI (ChatGPT/Codex — no key inside IronClaw)

A credential gateway is a host-local HTTP CONNECT proxy that holds the real credential and injects it per request, so neither the control-plane nor the sandbox ever sees a model key. This is how you power an agent with a ChatGPT/Codex account via OneCLI: IronClaw's codex provider speaks the ChatGPT Codex Responses API (chatgpt.com) and OneCLI attaches the OAuth credential.

Run OneCLI on the host (its default address is 127.0.0.1:10255), then point the model-proxy at it and allowlist the host it serves:

```sh

The gateway URL carries your per-agent OneCLI token as Basic userinfo — Go's HTTP

No ANTHROPIC_API_KEY needed — the gateway is the only credential path. Make the

🎯 aiskill88 AI 点评 A 级 2026-06-24

高质量的开源MCP工具,安全性强

⚡ 核心功能

👥 适合人群

Claude Desktop / Claude Code 用户AI 工具开发者需要扩展 AI 能力的专业人士自动化工程师

🎯 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

✅ 优点
  • +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
⚠️ 不足
  • 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
⚠️ 使用须知

该工具使用 AGPL-3.0 协议,商用场景请仔细阅读协议条款,必要时咨询法律意见。

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。

📄 License 说明

⚠️ AGPL 3.0 — 最严格的 Copyleft,网络服务端使用也需开源,SaaS 使用受限。

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❓ 常见问题 FAQ

MCP工具是一种安全优先的自托管AI代理平台
💡 AI Skill Hub 点评

经综合评估,铁爪 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。

⬇️ 获取与下载
⬇ 下载源码(GPL)
⚠️ 本工具使用 AGPL-3.0 协议。您可以自由下载和使用,但衍生作品必须以相同协议开源,不可商业闭源。使用前请确认符合协议要求。
📚 深入学习 铁爪
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 ironclaw
原始描述 开源MCP工具:Security-first, self-hosted AI agents - isolation you can prove, not just promis。⭐8 · Go
Topics ai-agentsai-assistantgolang
GitHub https://github.com/IronSecCo/ironclaw
License AGPL-3.0
语言 Go
🔗 原始来源
🐙 GitHub 仓库  https://github.com/IronSecCo/ironclaw 🌐 官方网站  https://ironsecco.github.io/ironclaw/

收录时间:2026-06-24 · 更新时间:2026-06-24 · License:AGPL-3.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。

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