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MCP工具

基于 Python · 让 AI 助手直接操作你的系统与工具

英文名：beever-atlas

⭐ 364 Stars 🍴 41 Forks 💻 Python 📄 Apache-2.0 🏷 AI 7.5分

7.5AI 综合评分

mcpadk-googlediscord-botfastapi

⬇ 下载源码 ZIP ⚙️ 配置说明

✦ AI Skill Hub 推荐

AI Skill Hub 推荐使用：MCP工具是一款优质的MCP工具。AI 综合评分 7.5 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。

📚 深度解析

MCP工具是一款基于 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 MCP工具，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。MCP工具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 MCP工具评为 AI 评分 7.5 分，属于同类工具中的优质选择。

📋 工具概览

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。

GitHub Stars

⭐ 364

开发语言

Python

支持平台

Windows / macOS / Linux

维护状态

轻量级项目，按需更新

开源协议

Apache-2.0

AI 综合评分

7.5 分

工具类型

MCP工具

Forks

📖 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」。

📌 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/Beever-AI/beever-atlas

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

# 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

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

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 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"

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": {
    "mcp__": {
      "command": "npx",
      "args": ["-y", "beever-atlas"],
      "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 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

Beever Atlas

Turn your team's Slack, Discord, Teams & Mattermost chats
into a self-maintaining wiki — automatically.

---

Beever Atlas pulls the conversations your team already has on Slack, Discord, Microsoft Teams, and Mattermost, extracts atomic facts, deduplicates them, and clusters them into topic pages with citations. A graph store links the people, decisions, and projects mentioned across channels. Ask questions in natural language and get answers cited back to the source messages — through the dashboard, or through MCP into Claude Code and Cursor.

If you want a knowledge base that grows on its own from the chats your team already has, this is it.

---

✨ Features in action

Six short clips — connect a workspace, sync history, watch memory build, browse the auto-generated wiki, ask questions, plug external AI agents in via MCP.

Multi-Platform Connect Slack, Discord, Teams, Mattermost, or file imports. One bot, every workspace.	Message Sync Pull channel history on demand or on a schedule. Resumable and rate-limit aware.	Memory Ingestion 6-stage ADK pipeline distils messages into atomic facts, entities, and relationships.
LLM Wiki Auto-maintained wiki per channel — overview, topics, people, decisions, citations.	QA Agent Streams cited answers over SSE. Smart router picks semantic or graph per question.	MCP Server Plug Claude Code / Cursor into your knowledge base — 16 tools, per-agent auth.

---

WEAVIATE_API_KEY — auth between backend and Weaviate (required by docker-compose)

python -c "import secrets; print(secrets.token_hex(16))"


Launch:

bash docker compose up -d --build ```

Open http://localhost:3000.

Services started:

Service	Port	Description
Web (nginx)	`:3000`	React dashboard
Backend	`:8000`	FastAPI + ADK agents
Bot	`:3001`	Platform bridge (Slack / Discord / Teams)
Weaviate	`:8080`	Semantic memory
Neo4j	`:7474` / `:7687`	Graph memory
MongoDB	`:27017`	State + wiki cache
Redis	`:6380`	Sessions (internal `:6379`)

First run takes 2–3 minutes while images build and databases initialize. Subsequent runs start in seconds.

4. Choose a deployment option

Option	When to use	Time to "up"
1. One-line install (recommended)	You want the fastest path to a running stack.	~2 min first run
2. Manual Docker	CI/CD, ops environments, or when you want explicit control over every step.	~3 min first run
3. Local development	Active contributors who need hot-reload on backend and frontend.	varies

Option 1 — One-line install (recommended)

./atlas

The atlas installer walks you through a guided 5-step checklist:

Embedding model — pick a provider (Jina / OpenAI / Cohere / Voyage / Gemini / Mistral / Ollama), then its API key.
Agent LLM provider — pick a provider for the 16 ADK agents (Google Gemini / OpenAI / Anthropic / Mistral / DeepSeek / Groq / MiniMax / Ollama / Custom); optional second provider for hybrid setups.
Graph backend — Neo4j (default) or skip.
Optional integrations — Tavily web search, MCP server for Claude Code / Cursor.
Auth tokens — keep dev defaults or rotate now.

Under the hood it verifies docker + docker compose, copies .env.example → .env (preserves your values on re-run, chmod 600), auto-generates CREDENTIAL_MASTER_KEY (64 hex) and WEAVIATE_API_KEY (32 hex), runs a port-conflict preflight, launches the stack via docker compose up -d --build --force-recreate --remove-orphans, and polls /api/health before printing the ready card.

When you see "Beever Atlas is ready", open http://localhost:3000 — then Settings → AI Setup to manage providers, assign LLMs per-agent, run Test Connection, or discover models. For CI / Docker / GitOps, configure declaratively: BEEVER_LLM_API_KEY=... (single-provider shortcut), BEEVER_ENDPOINTS='[...]' + BEEVER_PRESET=..., or commit an atlas.yaml and run atlas apply — see docs/runbooks/ai-setup.md and docs/runbooks/atlas-yaml.md.

For CI or unattended installs — skip prompts, pre-seed keys from shell env:

GOOGLE_API_KEY=... JINA_API_KEY=... ./atlas --non-interactive

Re-running ./atlas on an existing stack is idempotent.

Option 2 — Manual Docker

Full control, step-by-step.

cp .env.example .env

Open .env and fill in the two required keys:

GOOGLE_API_KEY=your_gemini_key
JINA_API_KEY=your_jina_key

Generate two required secrets and paste them into .env:

```bash

🚀 Quick Start

Beever Atlas ships as a Docker Compose stack (backend + bot + web + 4 datastores). You can try a seeded demo in 30 seconds with zero keys, then pick one of three deployment options to install it for real.

2. Try the demo first (optional, no keys needed for seeding)

make demo

make demo brings up the full stack pre-loaded with a public Wikipedia corpus (Ada Lovelace + Python history). Seeding uses pre-computed fixtures — no API keys required. Asking questions via /api/ask needs a free-tier GOOGLE_API_KEY because the QA agent calls Gemini. See demo/README.md for curl examples.

Skip this step if you're ready to install for real.

Option 3 — Local development

Databases in Docker, app services native for hot-reload.

Prerequisites: Python 3.12+ with uv, Node.js 20+

```bash cp .env.example .env

Fill in GOOGLE_API_KEY, JINA_API_KEY, CREDENTIAL_MASTER_KEY, WEAVIATE_API_KEY (same as Option 2)

3. Before you start: get your API keys

Two free keys are required before installing. Both offer generous free tiers — enough to sync a small team's channels for testing.

Key	Purpose	Where to get it
`GOOGLE_API_KEY`	Gemini — extraction, entity graph, answers	[aistudio.google.com/apikey](https://aistudio.google.com/apikey)
`JINA_API_KEY`	Jina v4 embeddings (2048-dim) for semantic search	[jina.ai/api-dashboard](https://jina.ai/api-dashboard/)

Optional (skip unless you know you need them):

Key	What it enables
`TAVILY_API_KEY`	External web search when QA retrieval confidence is low — [tavily.com](https://tavily.com/)
Slack / Discord / Teams bot tokens	Configured via the web UI after setup, not `.env` — the bot stores platform credentials encrypted in MongoDB

Tip: Keep the two required keys handy before you start. Option 1 prompts for them interactively; Options 2 and 3 need them pasted into .env.

📐 API Stability

All /api/* endpoints are UNSTABLE in 0.1.0. v0.2.0 will introduce a /api/v1/* prefix; clients pinning current paths will break. See SECURITY.md.

---

🎯 aiskill88 AI 点评 A 级 2026-06-01

高质量的MCP工具，支持LLM-Wiki对话知识库