什么是 io.github.ariekogan/ateam-mcp?
可在任意AI环境中构建、验证并部署多智能体AI解决方案,支持完整开发与交付流程。
README
ateam-mcp
Give any AI the ability to build, validate, and deploy production multi-agent systems.
This is an MCP server that connects AI assistants — ChatGPT, Claude, Gemini, Copilot, Cursor, Windsurf, and any MCP-compatible environment — directly to the ADAS platform.
An AI developer says "Build me a customer support system with order tracking and escalation" — and their AI assistant handles the entire lifecycle: reads the spec, builds skill definitions, validates them, deploys to production, and verifies health. No manual JSON authoring, no docs reading, no copy-paste workflows.
Why this matters
Today, building multi-agent systems requires deep platform knowledge, manual configuration, and switching between docs, editors, and dashboards. ateam-mcp eliminates all of that by making the ADAS platform a native capability of the AI tools developers already use.
The AI assistant becomes the developer interface:
Developer: "Create an identity verification agent that checks documents,
validates faces, and escalates fraud cases"
AI Assistant:
→ reads ADAS spec (adas_get_spec)
→ studies working examples (adas_get_examples)
→ builds skill + solution definitions
→ validates iteratively (adas_validate_skill, adas_validate_solution)
→ deploys to production (adas_deploy_solution)
→ verifies everything is running (adas_get_solution → health)
Developer: "Add a new skill that handles address verification"
AI Assistant:
→ deploys into the existing solution (adas_deploy_skill)
→ redeploys (adas_redeploy)
→ confirms health
No context switching. No manual steps. The full ADAS platform — specs, validation, deployment, monitoring — is available as natural language.
How it reaches the AI community
ChatGPT users
ChatGPT supports MCP connectors in Developer Mode. Users connect by pasting a single URL:
Settings → Connectors → Developer Mode → paste https://mcp.ateam-ai.com
That's it. All 12 ADAS tools appear in ChatGPT. Any ChatGPT Pro, Plus, Business, or Enterprise user can build and deploy multi-agent solutions through conversation.
Claude users
Claude Desktop — install as an extension (one-click) or add to config:
{
"mcpServers": {
"ateam": {
"command": "npx",
"args": ["-y", "@ateam-ai/mcp"],
"env": {
"ADAS_TENANT": "your-tenant",
"ADAS_API_KEY": "your-api-key"
}
}
}
}
Claude Code — one command:
claude mcp add ateam -- npx -y @ateam-ai/mcp
Cursor / Windsurf / VS Code (Copilot)
Add to .cursor/mcp.json, mcp_config.json, or .vscode/mcp.json:
{
"mcpServers": {
"ateam": {
"command": "npx",
"args": ["-y", "@ateam-ai/mcp"],
"env": {
"ADAS_TENANT": "your-tenant",
"ADAS_API_KEY": "your-api-key"
}
}
}
}
Gemini and other platforms
As MCP adoption grows (it's now governed by the Agentic AI Foundation under the Linux Foundation, co-founded by Anthropic, OpenAI, and Block), every AI platform that implements MCP gets access to ateam-mcp automatically. The remote HTTP endpoint (https://mcp.ateam-ai.com) works with any client that supports Streamable HTTP transport.
Discovery
Developers find ateam-mcp through:
- npm —
npm search mcp ai-agents→@ateam-ai/mcp - Official MCP Registry — registry.modelcontextprotocol.io
- Claude Desktop Extensions — built-in extension browser
- Claude Code Plugin Marketplace —
/plugin→ Discover tab - Windsurf MCP Marketplace — built-in marketplace
- VS Code MCP Gallery — Extensions view
- Community directories — Smithery, mcp.so, PulseMCP (30,000+ combined listings)
Available tools
| Tool | What it does |
|---|---|
adas_get_spec | Read the ADAS specification — skill schema, solution architecture, enums, agent guides |
adas_get_examples | Get complete working examples — skills, connectors, solutions |
adas_validate_skill | Validate a skill definition through the 5-stage pipeline |
adas_validate_solution | Validate a solution — cross-skill contracts + quality scoring |
adas_deploy_solution | Deploy a complete solution to production |
adas_deploy_skill | Add a skill to an existing solution |
adas_deploy_connector | Deploy a connector to ADAS Core |
adas_list_solutions | List all deployed solutions |
adas_get_solution | Inspect a solution — definition, skills, health, status, export |
adas_update | Update a solution or skill incrementally (PATCH) |
adas_redeploy | Push changes live — regenerates MCP servers, deploys to ADAS Core |
adas_solution_chat | Talk to the Solution Bot for guided modifications |
Setup
# Clone
git clone https://github.com/ariekogan/ateam-mcp.git
cd ateam-mcp
# Install
npm install
# Configure
cp .env.example .env
# Edit .env with your ADAS tenant and API key
# Run
npm start
Architecture
┌─────────────────────────────────────────────┐
│ AI Environment │
│ (ChatGPT / Claude / Cursor / Windsurf) │
│ │
│ Developer: "build me a support system" │
└──────────────────┬──────────────────────────┘
│ MCP protocol
│ (stdio or HTTP)
┌──────────────────▼──────────────────────────┐
│ ateam-mcp │
│ 12 tools — spec, validate, deploy, manage │
└──────────────────┬──────────────────────────┘
│ HTTPS
│ X-ADAS-TENANT / X-API-KEY
┌──────────────────▼──────────────────────────┐
│ ADAS External Agent API │
│ api.ateam-ai.com │
└──────────────────┬──────────────────────────┘
│
┌──────────────────▼──────────────────────────┐
│ ADAS Core │
│ Multi-agent runtime │
└─────────────────────────────────────────────┘
License
MIT
常见问题
io.github.ariekogan/ateam-mcp 是什么?
可在任意AI环境中构建、验证并部署多智能体AI解决方案,支持完整开发与交付流程。
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