AI Skill Hub 强烈推荐:Enola 是一款优质的MCP工具。AI 综合评分 8.0 分,在同类工具中表现稳健。如果你正在寻找可靠的MCP工具解决方案,这是一个值得深入了解的选择。
Enola 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。
Enola 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/enola-labs/enola
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"enola": {
"command": "npx",
"args": ["-y", "enola"]
}
}
}
# 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 Enola 执行以下任务... Claude: [自动调用 Enola 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": {
"enola": {
"command": "npx",
"args": ["-y", "enola"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
<p>enola - One graph of your whole software system, built from the code, on your machine</p>
<p align="center"> <a href="https://enola.tech">Website</a> · <a href="docs/README.md">Docs</a> · <a href="docs/CLI.md">CLI reference</a> · <a href="docs/BENCHMARKS.md">Benchmarks</a> · <a href="https://github.com/enola-labs/enola/releases">Releases</a> · <a href="https://github.com/enola-labs/enola/issues">Report a gap</a> </p>
<p> <a href="https://github.com/enola-labs/enola/actions/workflows/ci.yml"><img src="https://github.com/enola-labs/enola/actions/workflows/ci.yml/badge.svg" alt="CI"></a> <a href="https://github.com/enola-labs/enola/releases"><img src="https://img.shields.io/github/v/release/enola-labs/enola" alt="Release"></a> <a href="LICENSE"><img src="https://img.shields.io/github/license/enola-labs/enola" alt="License"></a> <a href="https://github.com/enola-labs/enola/stargazers"><img src="https://img.shields.io/github/stars/enola-labs/enola.svg?style=social&label=Star&maxAge=2592000" alt="GitHub stars"></a> <a href="https://mcptoplist.com/server/glama%2Fenola-labs%2Fenola"><img src="https://mcptoplist.com/badge/glama%2Fenola-labs%2Fenola.svg" alt="MCP Toplist"></a> </p>
<p>enola reads your source code and writes down what is in it: the modules, the functions, the API routes, the database tables it touches, the message topics it publishes. Then it records how each of those pieces connects to the others, inside one repository and across several. That record is a <em>graph</em>: a list of things, and a list of links between them. You can ask the graph questions, hand it to your coding agent, or build your own tools on it.</p>
<p><strong>Runs locally as one binary. No AI model, no language server, no account, no upload.</strong><br /> The graph comes from parsing your code. The same code always produces the same graph.</p>
<p align="center"> <img src="docs/images/layers-gate.gif" alt="enola check on the layers-gate example: a helper added to storage imports the delivery layer, the default run reports it and exits 0, --fail-on=layers fails the same change, and after the fix the re-run passes" width="80%" /> </p> </div>
A snapshot is a set of plain files with a documented format: the facts, the relationships between them, the findings, and a receipt recording exactly how it was built. Run enola as a subprocess and load those files wherever you need them.
Cognee builds its code-graph search this way: it pins an enola-cli release and loads the snapshot files, and that search needs no LLM key. How it finds the binary and what it writes: docs/INTEGRATING.md. If you got enola as part of Cognee, docs/COGNEE.md shows where the binary is and how to use it directly.
docs/GRAPH.md explains what the graph contains and which files are a stable contract; docs/INTEGRATING.md shows how to load it step by step.
1. Install enola. One prebuilt binary; no Go toolchain or C compiler needed.
curl -fsSL https://raw.githubusercontent.com/enola-labs/enola/main/install.sh | sh
This puts enola in ~/.local/bin. The same binary is on PyPI (pip install enola-cli) and RubyGems. Every install route, and how to upgrade, is in docs/CLI.md.
2. Point it at a repository you have checked out.
enola --explain /path/to/your/repo
No config file, no account, nothing written to disk. It reads the code and prints what it found. If you have nothing at hand, run it on enola itself:
git clone https://github.com/enola-labs/enola
enola --explain enola
Part of what that prints (the numbers move as the code does):
Overview
Languages: go, typescript, c, ruby, python
Total facts: 10814
Architecture
cyclic dependencies 0
layer violations 0
Impact analysis (hotspots)
Top hotspots (by coupling):
module fan-in fan-out crit blast radius
internal/facts 253 1 high 96
pkg/command 1 94 high 1
pkg/bootstrap 15 69 high 4
How to read it:
Total facts is how many it found.enola-intent.yaml; 0 means nothing in the code crosses it.fan-in is how many modules use it; fan-out is how many it uses; blast radius is how many modules a change to it can reach. Read the first row as: internal/facts is used from 253 places, and a change to it can reach 96 modules.If that is more vocabulary than you need, ask for the plain-language summary instead. It says what the repository is, whether its structure holds, where to start reading, what is risky to change and what is worth a look, and marks each statement with how sure enola is:
enola --explain --summary enola
3. Keep the graph and look at it. --explain only prints. To save the graph, build it with --generate. It is written under .enola/ in the repository, and that saved graph is what the local dashboard shows:
enola --generate .
enola dashboard --open
The dashboard is a local web page; nothing is sent anywhere. Dashboard guide.
Each example is a small repository plus a run.sh that reproduces the output shown in its README.
高质量的MCP工具,架构清晰
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。
✅ Apache 2.0 — 宽松开源协议,可商用,需保留版权声明和 NOTICE 文件,含专利授权条款。
总体来看,Enola 是一款质量优秀的MCP工具,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。
| 原始名称 | enola |
| 原始描述 | 开源MCP工具:enola - MCP Architectural Snapshot Server and Knowledge Graph。⭐45 · C |
| Topics | mcpai-toolsarchitecturecode-analysisdependency-graphgolangc |
| GitHub | https://github.com/enola-labs/enola |
| License | Apache-2.0 |
| 语言 | C |
收录时间:2026-06-25 · 更新时间:2026-06-26 · License:Apache-2.0 · 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