MCP工具 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。
MCP工具 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。
MCP工具 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。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/Sidd27/infrawise
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"mcp--": {
"command": "npx",
"args": ["-y", "infrawise"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 MCP工具 执行以下任务... Claude: [自动调用 MCP工具 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": {
"mcp__": {
"command": "npx",
"args": ["-y", "infrawise"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
<p align="center"> <a href="https://sidd27.github.io/infrawise/"> <img src="https://raw.githubusercontent.com/Sidd27/infrawise/main/website/public/logo-400.png" alt="Infrawise logo" width="130" /> </a> </p>
<p align="center"><b>Your AI coding assistant finally knows your infra.</b></p>
<p align="center"> <a href="https://www.npmjs.com/package/infrawise"><img src="https://img.shields.io/npm/v/infrawise" alt="npm version" /></a> <a href="https://github.com/Sidd27/infrawise/actions/workflows/npm-publish.yml"><img src="https://github.com/Sidd27/infrawise/actions/workflows/npm-publish.yml/badge.svg" alt="Publish to npm" /></a> <a href="https://github.com/Sidd27/infrawise/actions/workflows/ci.yml"><img src="https://github.com/Sidd27/infrawise/actions/workflows/ci.yml/badge.svg" alt="CI" /></a> <a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT" /></a> <a href="https://scorecard.dev/viewer/?uri=github.com/Sidd27/infrawise"><img src="https://api.securityscorecards.dev/projects/github.com/Sidd27/infrawise/badge" alt="OpenSSF Scorecard" /></a> <a href="https://glama.ai/mcp/servers/Sidd27/infrawise"><img src="https://glama.ai/mcp/servers/Sidd27/infrawise/badges/score.svg" alt="infrawise MCP server" /></a> <a href="https://www.producthunt.com/posts/infrawise"><img src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=1191162&theme=light" alt="Infrawise on Product Hunt" height="20" /></a> </p>
<p align="center"> <a href="https://sidd27.github.io/infrawise/">Website</a> · <a href="https://sidd27.github.io/infrawise/getting-started/installation/">Docs</a> · <a href="#quick-start">Quick start</a> </p>
Infrawise gives AI coding assistants deterministic infrastructure awareness.
It statically analyzes your codebase, cloud infrastructure, and database schemas, then exposes that context through MCP so tools like Claude Code can understand your actual tables, indexes, query patterns, and service relationships instead of guessing from source files alone.

---
Infrawise has two analysis layers:
Requires Node.js 22 or later (node --version).
npm install -g infrawise
or use without installing:
npx infrawise start --claude
---
infrawise check
Infrawise is read-only. Minimum IAM policy for DynamoDB:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": ["dynamodb:ListTables", "dynamodb:DescribeTable"],
"Resource": "*"
}
]
}
For the full policy across all supported services, how to scope it to only the services you enable, and using a session policy for temporary scoped credentials, see the AWS setup guide.
For SSO profiles, log in before running infrawise:
aws sso login --profile myprofile
Create a read-only user for infrawise:
CREATE USER infrawise_ro WITH PASSWORD 'yourpassword';
GRANT CONNECT ON DATABASE yourdb TO infrawise_ro;
GRANT USAGE ON SCHEMA public TO infrawise_ro;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO infrawise_ro;
For Amazon RDS: allow inbound on port 5432 from your machine's IP in the security group.
---
cd your-project
infrawise start --claude
That's it. Infrawise will:
infrawise.yaml (first time only — asks which AWS profile to use only if you have several).mcp.json so your editor auto-connects on every future launchEvery time after:
claude # no infrawise command needed — editor manages the connection
Analysis is cached for 24 hours. When the cache is stale, infrawise serve --stdio (spawned automatically by your editor) refreshes it at session start. File changes are detected within the session and the code graph is updated automatically.
Findings (3 total)
1. [HIGH] Full table scan detected on DynamoDB table "Orders"
listAllOrders() scans without any filter — reads every item in the table.
Recommendation: Replace Scan with Query using a partition key or add a GSI.
2. [MEDIUM] PostgreSQL table "users" has no index on column "email"
Filtering on "email" causes sequential scans.
Recommendation: CREATE INDEX CONCURRENTLY idx_users_email ON users(email);
3. [MEDIUM] DynamoDB table "Sessions" accessed by 6 distinct code paths
High access concentration may create hot partition issues at scale.
---
Two demos run infrawise against real AWS APIs emulated locally in Docker, at zero cost and with no real AWS account.
demo/floci/ uses Floci, an MIT-licensed emulator that covers every service infrawise supports — including CloudFront, API Gateway v2, RDS, Cognito, Kinesis, ElastiCache, and MSK. No auth token, no sign-up. Start here.demo/localstack/ uses LocalStack community edition, which covers the core services.Both listen on port 4566, so run one at a time.

---
| Flag | Description | ||
|---|---|---|---|
-c, --config <path> | Path to infrawise.yaml (default: infrawise.yaml) | ||
-r, --repo <path> | Repository to scan (default: current directory) | ||
--no-cache | Skip reading/writing the cache | ||
-o, --output <path> | Save findings as a markdown report, e.g. report.md | ||
--severity <level> | Only show findings at or above this level: high \ | medium \ | low |
```bash
check runs a fresh analysis and sets a non-zero exit code when blocking findings exist, so it can gate a pipeline without an AI editor.
| Flag | Description | ||
|---|---|---|---|
-c, --config <path> | Path to infrawise.yaml (default: infrawise.yaml) | ||
-r, --repo <path> | Repository to scan (default: current directory) | ||
--fail-on <level> | Severity that fails the build: high (default) \ | medium \ | low |
```bash
| Flag | Description |
|---|---|
-c, --config <path> | Path to infrawise.yaml (default: infrawise.yaml) |
--stdio | Use stdio transport (for editors via .mcp.json) instead of HTTP |
-p, --port <number> | Port to listen on, HTTP only (default: 3000) |
---
infrawise.yaml is generated by infrawise start (or infrawise start --interactive for the guided wizard) and lives in your repo root. Every service must be explicitly enabled: true — infrawise never connects to anything not listed in config.
Connection strings support ${ENV_VAR} substitution so passwords never need to be committed:
postgres:
enabled: true
connectionString: postgresql://infrawise_ro:${DB_PASSWORD}@host:5432/mydb
Full example:
project: payments-service
aws:
profile: default # AWS profile from ~/.aws/credentials
region: ap-south-1
dynamodb:
enabled: true
includeTables: # omit to include all tables
- Orders
- Users
postgres:
enabled: true
connectionString: postgresql://infrawise_ro:${DB_PASSWORD}@host:5432/mydb
mysql:
enabled: false
connectionString: ''
mongodb:
enabled: false
connectionString: ''
sqs:
enabled: true
sns:
enabled: true
ssm:
enabled: true
paths: [] # filter by prefix e.g. ["/myapp/prod"]
secretsManager:
enabled: true
lambda:
enabled: true
includeFunctions: # omit to include all functions
- myFunction
- anotherFunction
eventbridge:
enabled: true
rds:
enabled: false
s3:
enabled: false
apiGateway:
enabled: false
cognito:
enabled: false
kinesis:
enabled: false
msk:
enabled: false
elasticache:
enabled: false
cloudfront:
enabled: false
runtimeSignals:
enabled: false # Lambda throttles/errors + queue age via CloudWatch metrics
windowHours: 24
cloudwatchLogs:
enabled: false
logGroupPrefixes: []
windowHours: 24
analysis:
hotPartitionThreshold: 5
hotPartitionThresholds:
high-traffic-table: 12
freshness:
suggestRefreshAfterHours: 6 # when MCP responses start hinting to re-analyze
| Command | What it does |
|---|---|
infrawise start | **Primary command** — probe env, generate config, analyze, write editor MCP config |
infrawise start --claude | Same as above, then opens Claude Code |
infrawise start --cursor | Same as above, then opens Cursor |
infrawise start --vscode | Same as above, then opens VS Code (merges into .vscode/mcp.json) |
infrawise start --interactive | Run the guided setup wizard instead of auto-discovery |
infrawise start --rediscover | Delete infrawise.yaml + .infrawise/, then re-probe and re-analyze |
infrawise analyze | Force a full re-scan with extraction progress and a time estimate from past runs — useful after major infrastructure changes |
infrawise check | CI gate — analyze and exit non-zero when findings reach the threshold severity |
infrawise serve | Start the MCP server — HTTP by default, or --stdio for editor integration |
infrawise doctor | Diagnostic escape hatch — validate AWS/DB access, config, and repo scan |
infrawise start --vscode
Writes .vscode/mcp.json (merging with any existing MCP servers) and opens VS Code. The tools are available to Copilot agent mode via the MCP servers panel.
If your editor or workflow requires an HTTP MCP endpoint instead of stdio:
infrawise serve # starts server at http://localhost:3000/mcp
Add to your editor's MCP config:
{
"mcpServers": {
"infrawise": {
"url": "http://localhost:3000/mcp"
}
}
}
Infrawise 是一个基础设施分析工具,可以扫描和理解你的 AWS 服务、数据库和代码库。它通过 MCP(Model Context Protocol)与 Claude Code 编辑器集成,提供 15 个专用工具帮助开发者快速分析基础设施配置。项目采用 npm 发布,支持全局安装或通过 npx 直接运行,无需本地安装即可使用。
Infrawise 提供两层分析能力:首先扫描 AWS 服务配置(如 DynamoDB、RDS 等),其次分析 PostgreSQL 数据库结构和代码库内容。所有操作均为只读模式,确保安全性。通过生成 `.mcp.json` 配置文件,自动与 Claude Code 编辑器集成,开发者可在编辑器中直接访问所有分析工具,无需额外配置。
安装 Infrawise 有两种方式:全局安装使用 `npm install -g infrawise`,或通过 `npx infrawise start --claude` 直接运行无需安装。AWS 环境需配置最小 IAM 权限(仅允许 DynamoDB 列表和描述操作)。PostgreSQL 可选,需创建只读用户并授予 SELECT 权限。若使用 AWS SSO,需先执行 `aws sso login --profile` 登录。
进入项目目录执行 `infrawise start --claude` 启动。首次运行会提示配置问题并生成 `infrawise.yaml` 配置文件,随后扫描 AWS 服务、数据库和代码库。工具自动生成 `.mcp.json` 供编辑器使用,并打开 Claude Code 加载全部 15 个 MCP 工具。后续只需在编辑器中运行 `claude` 命令,无需重复执行 infrawise 命令。
`infrawise.yaml` 由 `infrawise start` 自动生成,位于项目根目录。所有服务必须显式设置 `enabled: true` 才会被连接,确保安全性。连接字符串支持 `${ENV_VAR}` 环境变量替换,密码等敏感信息无需提交到代码库。主要配置选项包括:`-c` 指定配置文件路径、`-r` 指定扫描仓库、`--no-cache` 跳过缓存、`-o` 指定输出路径。
CLI 主要命令:`infrawise start` 为核心命令,执行初始化、分析和编辑器配置写入;`infrawise start --claude` 在上述基础上自动打开 Claude Code;`infrawise init` 仅生成配置文件;`infrawise analyze` 执行分析操作。所有命令均支持通过标志位自定义行为,如指定配置文件路径、仓库位置、缓存策略和输出格式。
高质量的MCP工具,支持AWS基础设施分析
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。
经综合评估,MCP工具 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 | infrawise |
| 原始描述 | 开源MCP工具:MCP server for AWS infrastructure analysis — Lambda, DynamoDB, SQS, PostgreSQL, 。⭐13 · TypeScript |
| Topics | awstypescriptmcp |
| GitHub | https://github.com/Sidd27/infrawise |
| License | MIT |
| 语言 | TypeScript |
收录时间:2026-06-11 · 更新时间:2026-06-11 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端