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agent-deploy-dashboard-mcp

Unified deployment dashboard for Vercel, Render, Railway and Fly.io: deploy status, logs, environment variables, rollbacks and health checks.

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About this tool

Purpose

agent-deploy-dashboard-mcp is a unified deployment management MCP server, used to let AI agents manage services on Vercel, Render, Railway, and Fly.io from a single MCP + REST API.

Main Capabilities

  • Multi-platform support: manage Vercel, Render, Railway, and Fly.io in the same interface.
  • Deployment status: check deployment status and health across all platforms.
  • Unified logs: trace logs and view build logs.
  • Environment management: list, update, and manage environment variables.
  • Redeploy operations: trigger redeploys and rollbacks.
  • x402 micropayments: built-in payment middleware for API monetization.
  • Rate limiting: 50 free requests per IP per day, with paid tier support.

Tool Reference

  1. list_all_services(): List all services across all platforms. Parameters: none.
  2. get_deploy_status(platform, service_id): Check the deployment status of the specified service. Parameters: platform (string, platform name - vercel, render, railway, or fly), service_id (string, service/project ID).
  3. tail_logs(platform, service_id, lines=100): Stream recent logs for a service. Parameters: platform (string, platform name), lines (integer, optional, number of log lines, default 100).
  4. get_env_vars(platform, service_id): List the environment variables of a service.
  5. set_env_var(platform, service_id, key, value): Update an environment variable. Parameters: key (string, environment variable name), value (string, environment variable value).
  6. trigger_redeploy(platform, service_id): Force redeploy a service.
  7. get_build_logs(platform, deploy_id): Get the build logs of a deployment. Parameters: deploy_id (string, deployment ID).
  8. check_health(url): Ping a health check endpoint. Parameters: url (string, the URL to check).
  9. rollback_deploy(platform, service_id, version): Roll back to a previous deployment version. Parameters: version (string, the version/deployment ID to roll back to).

Usage

MCP configuration: Add the following content to the MCP settings file (cline_mcp_settings.json or similar). Base URL:https://agent-deploy-dashboard-mcp.onrender.com。免费层级包含：每个 50 requests per IP per day, includes all tools, supports all platforms, no credit card required.

Paid tier (HTTP 402 payment): After the free tier is exhausted, each request costs $0.01, paid via HTTP 402 using a crypto wallet. Wallet address: 0x8E844a7De89d7CfBFe9B4453E65935A22F146aBB. Include the X-Payment header in the request and provide proof of payment.

Deploy on Render: 1. Fork this repository to your GitHub account; 2. Go to the Render Dashboard; 3. Click New → Web Service; 4. Connect your GitHub repository: aparajithn/agent-deploy-dashboard-mcp; 5. Configure: Name: agent-deploy-dashboard-mcp, Runtime: Docker, Region: Oregon (or the nearest region), Instance Type: Free; 6. Add environment variables: VERCEL_TOKEN, RENDER_API_KEY, RAILWAY_TOKEN (optional), FLY_API_TOKEN (optional), X402_WALLET_ADDRESS (optional), PUBLIC_HOST = agent-deploy-dashboard-mcp.onrender.com; 7. Click Create Web Service.

Environment variables: Configure platform API access through environment variables. Get API tokens: Vercel:https://vercel.com/account/tokens；Render：https://dashboard.render.com/u/settings#api-keys；Railway：https://railway.app/account/tokens；Fly.io：运行 flyctl auth token.

Tech stack: Python 3.11 (modern async/await), FastAPI (REST API framework), FastMCP (MCP protocol implementation, Streamable HTTP), httpx (fast asynchronous HTTP client), Pydantic (data validation).

API reference documentation: After deployment, you can view the interactive API documentation at: Swagger UI:https://agent-deploy-dashboard-mcp.onrender.com/docs；OpenAPI JSON：https://agent-deploy-dashboard-mcp.onrender.com/openapi.json。

Use Cases

AI agent workflows that need to centrally manage service status, logs, environment variables, and deployment operations across multiple deployment platforms.

Limitations

Platform support status is divided into three levels: ✅ indicates fully implemented, ⚠️ indicates partially implemented, ⏳ indicates planned/stub implementation; however, the materials do not specify which status each platform is in. The project uses the MIT License; see LICENSE for details. Feedback: GitHub Issues; email: aparajith@dabaracoffee.com. Built for AI agents by Forge (Aparajith's coding agent).

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