Ateam MCP Server

Build, validate, and deploy multi-agent AI solutions from any AI environment.

Tools

Last Updated

Jun 9, 2026

What is the Ateam MCP Server?

The Ateam MCP server gives AI agents structured, permission-aware access to Ateam through the Model Context Protocol. With 40 pre-built actions, agents can read, create, and update Ateam data on behalf of authorized users.

Willow ships the Ateam MCP server as part of an enterprise control plane. Every call runs behind SSO (Okta, Azure AD), enforces RBAC and least-privilege at runtime, writes to a full audit trail, and integrates with Splunk and Loki for SIEM visibility. Connect from Claude Desktop, Claude Code, Cursor, ChatGPT, VS Code, n8n, or any custom agent. Install once, distribute org-wide, and see exactly how Ateam is being used by every AI agent in your stack.

Tools

ateam_bootstrap

REQUIRED onboarding entrypoint for A-Team MCP. MUST be called when user greets, says hi, asks what this is, asks for help, explores capabilities, or when MCP is first connected. Returns platform explanation, example solutions, and assistant behavior instructions. Do NOT improvise an introduction — call this tool instead.

ateam_auth

Authenticate with A-Team. Required before any tenant-aware operation (reading solutions, deploying, testing, etc.). The user can get their API key at https://mcp.ateam-ai.com/get-api-key. Only global endpoints (spec, examples, validate) work without auth. IMPORTANT: Even if environment variables (ADAS_API_KEY) are configured, you MUST call ateam_auth explicitly — env vars alone are not sufficient. For cross-tenant admin operations, use master_key instead of api_key.

ateam_get_spec

Get the A-Team specification — schemas, validation rules, system tools, agent guides, and templates. Start here after bootstrap to understand how to build skills and solutions. Use 'section' to get just one part of the skill spec (much smaller than the full spec). Use 'search' to find specific fields or concepts across the spec.

ateam_get_workflows

Get the builder workflows — step-by-step state machines for building skills and solutions. Use this to guide users through the entire build process conversationally. Returns phases, what to ask, what to build, exit criteria, and tips for each stage.

ateam_get_examples

Get complete working examples that pass validation. Study these before building your own.

ateam_build_and_run

DEPLOY THE CURRENT MAIN BRANCH TO A-TEAM CORE. ⚠️ HEAVIEST OPERATION (60-180s): validates solution+skills → deploys all connectors+skills to Core (regenerates MCP servers) → health-checks → optionally runs a warm test → auto-pushes to GitHub. 🌳 DEV/PROD WORKFLOW: 1. Edit files → ateam_github_patch (writes to `dev` branch by default) 2. (Optional) Preview what's about to ship → ateam_github_diff 3. Ship dev → main → ateam_github_promote (merges + auto-tags `prod-YYYY-MM-DD-NNN`) 4. Deploy main to Core → ateam_build_and_run This tool ALWAYS deploys the `main` branch — there is no `ref` parameter. To deploy in-progress dev work, first promote it. AUTO-DETECTS GitHub repo: if you omit mcp_store and a repo exists, connector code is pulled from main automatically. First deploy requires mcp_store. After that, edit via ateam_github_patch + promote, then build_and_run. For small changes prefer ateam_patch (faster, incremental). Requires authentication.

ateam_test_skill

Send a test message to a deployed skill and get the full execution result. By default waits for completion (up to 60s). Set wait=false for async mode — returns job_id immediately, then poll with ateam_test_status.

ateam_conversation

Send a message to a deployed solution and get the result. No skill_id needed — the system auto-routes to the right skill. Supports multi-turn conversations: pass the actor_id from a previous response to continue the thread (e.g., reply to a confirmation prompt). Each call creates a new job but the same actor_id maintains conversation context.

ateam_test_pipeline

Test the decision pipeline (intent detection → planning) for a skill WITHOUT executing tools. Returns intent classification, first planned action, and timing. Use this to debug why a skill classifies intent incorrectly or plans the wrong action.

ateam_test_voice

Simulate a voice conversation with a deployed solution. Runs the full voice pipeline (session → caller verification → prompt → skill dispatch → response) using text instead of audio. Returns each turn with bot response, verification status, tool calls, and entities. Use this to test voice-enabled solutions end-to-end without making a phone call.

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Set Up Your Ateam MCP Server in Minutes

Add the following configuration to your MCP client. Authentication is handled via OAuth. Compatible with Claude Desktop, Claude Code, Cursor, ChatGPT, VS Code, n8n, and any MCP-compatible agent.

Claude Desktop

claude_desktop_config.json

{
  "mcpServers": {
    "willow-ateam": {
      "type": "http",
      "url": "https://<org>.mcp-s.com/mcp/mcp/ateam"
    }
  }
}

Cursor

.cursor/mcp.json

{
  "mcpServers": {
    "willow-ateam": {
      "type": "http",
      "url": "https://<org>.mcp-s.com/mcp/mcp/ateam"
    }
  }
}

Claude Code

CLI

claude mcp add willow-ateam --transport http https://<org>.mcp-s.com/mcp/mcp/ateam

n8n

HTTP Request Node

{
  "url": "https://<org>.mcp-s.com/mcp/mcp/ateam",
  "method": "POST"
}

Or click "Install with Willow" above to set up automatically with SSO and RBAC preconfigured.

Enterprise Governance for Ateam

Willow adds the layer Ateam and every other SaaS doesn't ship out of the box: every call runs behind SSO (Okta, Azure AD), enforces RBAC and least-privilege at runtime, writes to full audit logs, and detects shadow AI usage across your stack. One MCP gateway. Any agent. Every tool.

Ateam MCP Server FAQ

What is the Ateam MCP server?

The Ateam MCP server is a Model Context Protocol implementation that lets AI agents like Claude, Cursor, and ChatGPT read and write Ateam data through a standardized interface. Willow hosts and governs this server so enterprises can roll it out without a security review backlog.

How is Willow's Ateam MCP server different from the official one?

The official Ateam MCP server is scoped to a single user's account and does not include enterprise governance. Willow's version adds SSO, RBAC, audit logging, shadow AI detection, and centralized control over which actions agents can take across the entire org.

Which AI clients work with the Ateam MCP server?

Claude Desktop, Claude Code, Cursor, ChatGPT, VS Code with MCP support, n8n, and any custom agent built with OpenAI Agents SDK, LangChain, Vercel AI SDK, or Anthropic SDK.

Is the Ateam MCP server secure? How does Willow handle authentication?

Every call runs behind your existing SSO (Okta, Azure AD). Per-user OAuth scopes the agent to exactly what that user can do in Ateam, nothing more. No credentials reach the LLM. Every action writes to an audit trail.

Can I limit which Ateam actions agents can take?

Yes. Willow lets you scope agents to specific actions, specific projects, or specific environments. Toggle actions on or off in the dashboard, or enforce policy via infrastructure-as-code through GitHub.

How do I detect shadow Ateam MCP servers in my org?

Willow's browser extension and discovery service surface unmanaged MCP servers, skills, and AI agents across the org. If a developer installed an unapproved Ateam MCP locally, you'll see it.

What does the Ateam MCP server cost?

Pricing depends on org size and deployment model (SaaS, dedicated cloud, self-host). See withwillow.ai/pricing or contact sales for a quote.

How do I install the Ateam MCP server with Willow?

Install via the Willow Connect Panel in one click, or paste the JSON snippet above into your Claude Desktop, Cursor, or Claude Code config. SSO and RBAC inherit from your existing Willow setup.