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deploy-mcp/deploy-mcp

by Various

Universal deployment tracker for AI assistants - check deployment status without leaving your AI chat

D

MCP

deploy-mcp/deploy-mcp

Added 1 June 2026

#ai #ci-cd #claude #cloudflare-deployment #cloudflare-pages #deployment #deployment-monitor #deployment-status

Overview

deploy-mcp is a TypeScript-based Model Context Protocol server that lets AI assistants check deployment status directly within a chat interface. It provides a universal deployment tracker, enabling users to query deployment information without switching contexts.

Best for

Best for
Developers who want to check deployment status from within their AI chat interface

Use cases

  • Check deployment status from within an AI chat session
  • Monitor deployments without leaving the development workflow
  • Integrate deployment tracking into MCP-compatible AI assistants

How to use

Install

npx deploy-mcp

Tools exposed

  • check_deployment_status
  • watch_deployment
  • compare_deployments
  • get_deployment_logs
  • list_projects

Tested with

Claude Desktop, Cursor, Windsurf, Cline, Continue, VS Code

Notes

deploy-mcp is a TypeScript-based Model Context Protocol server that lets AI assistants check deployment status directly within a chat interface. It provides a universal deployment tracker, enabling users to query deployment information without switching contexts.

7 stars on GitHub. Last updated 2026-05-09. Licensed Apache-2.0.

Use cases

  • Check deployment status from within an AI chat session
  • Monitor deployments without leaving the development workflow
  • Integrate deployment tracking into MCP-compatible AI assistants

Pros

  • Simple integration with MCP-compatible AI assistants
  • Lightweight TypeScript implementation
  • Open source with a clear focus on deployment status

Cons

  • Low star count indicates limited adoption and community
  • Requires an MCP-compatible AI assistant to function
  • Only provides deployment status, not full CI/CD pipeline details

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Simple integration with MCP-compatible AI assistants
  • Lightweight TypeScript implementation
  • Open source with a clear focus on deployment status

Cons

  • Low star count indicates limited adoption and community
  • Requires an MCP-compatible AI assistant to function
  • Only provides deployment status, not full CI/CD pipeline details
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