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Build, validate, and deploy multi-agent AI solutions from any AI environment.
About
Build, validate, and deploy multi-agent AI solutions from any AI environment.
Remote endpoints: streamable-http: https://mcp.ateam-ai.com/mcp
Security Report
Valid MCP server (1 strong, 1 medium validity signals). 2 known CVEs in dependencies (0 critical, 2 high severity) Package registry verified. Imported from the Official MCP Registry.
4 files analyzed · 3 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
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How to Install & Connect
Available as Local & Remote
This plugin can run on your machine or connect to a hosted endpoint. during install.
Documentation
View on GitHubFrom the project's GitHub 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.ai and Claude Desktop — add the hosted connector: open Customize > Connectors (Claude Desktop: Customize in the sidebar, then Connectors), click Add custom connector, enter https://mcp.ateam-ai.com, and click Connect. The A-Team sign-in page opens in the browser and asks for your workspace's key: copy it from app.ateam-ai.com/connect (a workspace owner or admin) and paste it there. The key decides the workspace; to switch, Disconnect and Connect again with the other workspace's key.
Claude Code — the hosted connector, signed in the same way:
claude mcp add --transport http ateam https://mcp.ateam-ai.com
then /mcp and authenticate in the browser.
Never paste a key into a chat. ateam_bootstrap's session field says which workspace a session is on and how to switch.
Cursor / Windsurf / VS Code (Copilot)
Add the hosted server to .cursor/mcp.json, mcp_config.json, or .vscode/mcp.json:
{
"mcpServers": {
"ateam": { "url": "https://mcp.ateam-ai.com/mcp" }
}
}
A client with OAuth opens the A-Team sign-in page; one without sends the key as Authorization: Bearer <key> from its own config (see below). A local process (npx -y @ateam-ai/mcp) signs in only through ateam_auth: an ADAS_API_KEY in its environment does not sign it in.
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, also at /mcp) works with any client that supports Streamable HTTP transport.
The HTTP endpoint needs a credential on every request: OAuth (clients that support it follow the 401 challenge on their own), or your A-Team API key sent as Authorization: Bearer <key>. An anonymous request is refused with 401; since 0.4.93 there is no anonymous session to call ateam_auth from. See CHANGELOG.md.
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 |
ateam_workspace_open | For a chat agent with no shell: open one private workspace (files in /workspace) for the conversation |
ateam_workspace_exec | Run one command in the workspace (100 s foreground; run_in_background for long builds) |
ateam_workspace_status | Check the workspace: unsaved changes, a long save or background command |
ateam_workspace_save | Make the workspace's work live (the only way the other tools see it) |
ateam_workspace_close | Finish; optionally save first. Brings back the single-file write tools withheld while it was open |
The ateam_workspace_* tools are listed only when the platform says workspaces are available for the signed-in workspace (they are switched off by default); otherwise they are absent.
Setup
# Clone
git clone https://github.com/ariekogan/ateam-mcp.git
cd ateam-mcp
# Install
npm install
# Run (a local stdio process)
npm start
A local process signs in with ateam_auth, given a workspace key the agent reads from a file outside the chat; an ADAS_API_KEY in the environment does not sign it in and is never sent. ADAS_API_URL in its environment points it at a self-hosted API. Most people use the hosted connector instead (above).
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
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