Bridges Claude, ChatGPT, Cursor, and other AI assistants directly to the ADAS multi-agent platform. Exposes twelve tools that let your AI read the ADAS spec, validate skill and solution definitions through a five-stage pipeline, deploy to production, and monitor health. The workflow is conversational: describe a customer support system with escalation, and the assistant builds the skill definitions, validates them, deploys, and confirms everything is running. No manual JSON authoring or dashboard hopping. Works over stdio for local setups or streamable HTTP at mcp.ateam-ai.com for remote access. Particularly useful if you're building production agent systems on ADAS and want to stay in your editor or chat interface instead of context switching to docs and deployment tools.
Public tool metadata for what this MCP can expose to an agent.
adas_get_specGet the ADAS specification — schemas, validation rules, system tools, agent guides, and templates. Use this to understand how to build skills and solutions.1 paramsGet the ADAS specification — schemas, validation rules, system tools, agent guides, and templates. Use this to understand how to build skills and solutions.
topicstringoverview · skill · solution · enumsadas_get_examplesGet complete working examples that pass validation. Study these before building your own.1 paramsGet complete working examples that pass validation. Study these before building your own.
typestringskill · connector · connector-ui · solution · indexadas_validate_skillValidate a skill definition through the 5-stage ADAS validation pipeline. Returns errors and suggestions to fix.1 paramsValidate a skill definition through the 5-stage ADAS validation pipeline. Returns errors and suggestions to fix.
skillobjectadas_validate_solutionValidate a solution definition — cross-skill contracts, grant economy, handoffs, and LLM quality scoring.2 paramsValidate a solution definition — cross-skill contracts, grant economy, handoffs, and LLM quality scoring.
skillsarraysolutionobjectadas_deploy_solutionDeploy a complete solution to ADAS Core — identity, connectors, skills. The Skill Builder auto-generates MCP servers from tool definitions. This is the main deployment action.4 paramsDeploy a complete solution to ADAS Core — identity, connectors, skills. The Skill Builder auto-generates MCP servers from tool definitions. This is the main deployment action.
skillsarraysolutionobjectmcp_storeobjectconnectorsarrayadas_deploy_skillDeploy a single skill into an existing solution.2 paramsDeploy a single skill into an existing solution.
skillobjectsolution_idstringadas_deploy_connectorDeploy a connector — registers in the Skill Builder catalog and connects in ADAS Core.1 paramsDeploy a connector — registers in the Skill Builder catalog and connects in ADAS Core.
connectorobjectadas_list_solutionsList all solutions deployed in the Skill Builder.List all solutions deployed in the Skill Builder.
No parameter schema in public metadata yet.
adas_get_solutionRead solution state — definition, skills, health, status, or export. Use this to inspect deployed solutions.3 paramsRead solution state — definition, skills, health, status, or export. Use this to inspect deployed solutions.
viewstringdefinition · skills · health · status · export · validateskill_idstringsolution_idstringadas_updateUpdate a deployed solution or skill incrementally using PATCH. Supports dot notation for scalar fields and _push/_delete/_update for arrays.4 paramsUpdate a deployed solution or skill incrementally using PATCH. Supports dot notation for scalar fields and _push/_delete/_update for arrays.
targetstringsolution · skillupdatesobjectskill_idstringsolution_idstringadas_redeployRe-deploy after making updates. Regenerates MCP servers and pushes to ADAS Core.2 paramsRe-deploy after making updates. Regenerates MCP servers and pushes to ADAS Core.
skill_idstringsolution_idstringadas_solution_chatSend a message to the Solution Bot — an AI assistant that understands your deployed solution and can help with modifications.2 paramsSend a message to the Solution Bot — an AI assistant that understands your deployed solution and can help with modifications.
messagestringsolution_idstringGive 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.
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.
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 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
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"
}
}
}
}
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.
Developers find ateam-mcp through:
npm search mcp ai-agents → @ateam-ai/mcp/plugin → Discover tab| 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 |
# 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
┌─────────────────────────────────────────────┐
│ 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 │
└─────────────────────────────────────────────┘
MIT
io.github.ericm1018/skillfm-llm-cost-optimizer-openai-anthropic-usage
io.github.mikerawsonnz/llm-orchestration-agent
io.github.mikerawsonnz/authenticated-llm-agent
labforgedev/copilot-memory-mcp
csoai-org/agent-prompt-injection-firewall-mcp
io.github.mikerawsonnz/authenticated-multi-llm-agent